This paper presents gossip algorithms for aggregation tasks that demonstrate both robustness to adversarial corruptions of any order of magnitude and optimality across a substantial range of these corruption levels. Gossip algorithms distribute information in a scalable and efficient way by having random pairs of nodes exchange small messages. Value aggregation problems are of particular interest in this setting, as they occur frequently in practice, and many elegant algorithms have been proposed for computing aggregates and statistics such as averages and quantiles. An important and well-studied advantage of gossip algorithms is their robustness to message delays, network churn, and unreliable message transmissions. However, these crucial robustness guarantees only hold if all nodes follow the protocol and no messages are corrupted. In this paper, we remedy this by providing a framework to model both adversarial participants and message corruptions in gossip-style communications by allowing an adversary to control a small fraction of the nodes or corrupt messages arbitrarily. Despite this very powerful and general corruption model, we show that robust gossip algorithms can be designed for many important aggregation problems. Our algorithms guarantee that almost all nodes converge to an approximately correct answer with optimal efficiency and essentially as fast as without corruptions. The design of adversarially-robust gossip algorithms poses completely new challenges. Despite this, our algorithms remain very simple variations of known non-robust algorithms with often only subtle changes to avoid non-compliant nodes gaining too much influence over outcomes. While our algorithms remain simple, their analysis is much more complex and often requires a completely different approach than the non-adversarial setting.

We aim to revive Thorup’s conjecture [Thorup, WG’92] on the existence of reachability shortcuts with ideal size-diameter tradeoffs. Thorup originally asked whether, given any graph G=(V,E) with m edges, we can add m1+o(1) “shortcut” edges E+ from the transitive closure E* of G so that G+(u,v) ≤ mo(1) for all (u,v)∈ E*, where G+=(V,E∪ E+). The conjecture was refuted by Hesse [Hesse, SODA’03], followed by significant efforts in the last few years to optimize the lower bounds.

We present the first deterministic nearly-linear time algorithm for single-source shortest paths with negative edge weights on directed graphs: given a directed graph G with n vertices, m edges whose weights are integer in {−W,…,W}, our algorithm either computes all distances from a source s or reports a negative cycle in time O(m)· log(nW) time.

We show that every directed graph G with n vertices and m edges admits a directed acyclic graph (DAG) with m1+o(1) edges, called a DAG projection, that can either (1+1/polylog (n))-approximate distances between all pairs of vertices (s,t) in G, or no(1)-approximate maximum flow between all pairs of vertex subsets (S,T) in G. Previous similar results suffer a Ω(logn) approximation factor for distances [Assadi, Hoppenworth, Wein, STOC’25] [Filtser, SODA’26] and, for maximum flow, no prior result of this type is known.

A fault-tolerant distance labeling scheme assigns a label to each vertex and edge of an undirected weighted graph G with n vertices so that, for any edge set F of size |F| ≤ f, one can approximate the distance between p and q in G ∖ F by reading only the labels of F ∪ {p,q}.

Graph decompositions are powerful algorithmic tools with wide applications to graph structures (e.g., spanners, hopsets, sparsifiers, oblivious routings, etc.) and network optimization algorithms, including parallel, distributed and dynamic algorithms for flow and distance problems.- Classical graph decompositions include- low-diameter decomposition, which captures l(1)-quantities like lengths and costs, andexpander decomposition, which captures l(infinity)-quantities like flows and congestion.This keynote starts with a brief survey of these classical decompositions, then presents length-constrained expanders and length-constrained expander decompositions - a recent and technically rich generalization that simultaneously controls length and congestion ( l(1) & l(infinity)). Length-constrained expander decompositions significantly broaden and extend the range of applications for graph decompositions, and this talk will discuss several examples and ways to leverage their power. 2012 ACM Subject Classification Theory of computation -> Graph algorithms analysis

We show the existence of length-constrained expander decomposition in directed graphs and undirected vertex-capacitated graphs. Previously, its existence was shown only in undirected edge-capacitated graphs [Bernhard Haeupler et al., 2022; Haeupler et al., 2024]. Along the way, we prove the multi-commodity maxflow-mincut theorems for length-constrained expansion in both directed and undirected vertex-capacitated graphs. Based on our decomposition, we build a length-constrained flow shortcut for undirected vertex-capacitated graphs, which roughly speaking is a set of edges and vertices added to the graph so that every multi-commodity flow demand can be routed with approximately the same vertex-congestion and length, but all flow paths only contain few edges. This generalizes the shortcut for undirected edge-capacitated graphs from [Bernhard Haeupler et al., 2024]. Length-constrained expander decomposition and flow shortcuts have been crucial in the recent algorithms in undirected edge-capacitated graphs [Bernhard Haeupler et al., 2024; Haeupler et al., 2024]. Our work thus serves as a foundation to generalize these concepts to directed and vertex-capacitated graphs.

We present a parallel algorithm for computing ($1+ \epsilon$)-approximate min-cost flow on an undirected graph with m edges, where capacities and costs are assigned to both edges and vertices. Our algorithm achieves $\hat{O}(m)$ work and $\hat{O}(1)$ depth when $\epsilon\gt 1 / \operatorname{polylog}(m)$, making both the work and depth almost optimal, up to a subpolynomial factor. Previous algorithms with $\hat{O}(m)$ work required $\Omega(m)$ depth, even for special cases of min-cost flow with only edge capacities or max flow with vertex capacities. Our result generalizes prior almost-optimal parallel $(1+\epsilon)$-approximation algorithms for these special cases, including shortest paths [1]–[3] and max flow with only edge capacities [4], [5]. Our key technical contribution is the first construction of length-constrained flow shortcuts with $(1+\epsilon)$ length slack, $\hat{O}(1)$ congestion slack, and $\hat{O}(1)$ step bound. This provides a strict generalization of the influential concept of $(\hat{O}(1), \epsilon)$-hopsets [6], allowing for additional control over congestion. Previous lengthconstrained flow shortcuts [7] incur a large constant in the length slack, which would lead to a large approximation factor. To enable our flow algorithms to work under vertex capacities, we also develop a close-to-linear time algorithm for computing length-constrained vertex expander decomposition. Building on Cohen’s idea of path-count flows [8], we further extend our algorithm to solve $(1+\epsilon)$-approximate k-commodity min-cost flow problems with almost-optimal $\hat{O}(m k)$ work and $\hat{O}(1)$ depth, independent of the number of commodities k.

Low Diameter Decompositions (LDDs) are invaluable tools in the design of combinatorial graph algorithms. While historically they have been applied mainly to undirected graphs, in the recent breakthrough for the negative-length Single Source Shortest Path problem, Bernstein, Nanongkai, and Wulff-Nilsen [FOCS '22] extended the use of LDDs to directed graphs for the first time. Specifically, their LDD deletes each edge with probability at most O(1/D ⋅ log²n), while ensuring that each strongly connected component in the remaining graph has a (weak) diameter of at most D. In this work, we make further advancements in the study of directed LDDs. We reveal a natural and intuitive (in hindsight) connection to Expander Decompositions, and leveraging this connection along with additional techniques, we establish the existence of an LDD with an edge-cutting probability of O(1/D ⋅ log n log log n). This improves the previous bound by nearly a logarithmic factor and closely approaches the lower bound of Ω(1/D ⋅ log n). With significantly more technical effort, we also develop two efficient algorithms for computing our LDDs: a deterministic algorithm that runs in time Õ(m poly(D)) and a randomized algorithm that runs in near-linear time Õ(m). We believe that our work provides a solid conceptual and technical foundation for future research relying on directed LDDs, which will undoubtedly follow soon.

This paper extends and generalizes the well-known cut-matching game framework and provides a novel cut-strategy that produces constant-hop expanders.

We consider the problem of sorting n items, given the outcomes of m pre-existing comparisons. We present a simple and natural deterministic algorithm that runs in O(m + log T ) time and does O(log T ) comparisons, where T is the number of total orders consistent with the pre-existing comparisons.

This paper presents parallel, distributed and quantum algorithms for single-source shortest paths when edges can have negative weights (negative-weight SSSP). We show a framework that reduces negative-weight SSSP in all these setting to $n^{o(1)}$ calls to any SSSP algorithm that works with a virtual source. More specifically, for a graph with $m$ edges, $n$ vertices, undirected hop-diameter $D$, and polynomially bounded integer edge weights, we show randomized algorithms for negative-weight SSSP with (i) $W_{SSSP}(m,n)n^{o(1)}$ work and $S_{SSSP}(m,n)n^{o(1)}$ span, given access to an SSSP algorithm with $W_{SSSP}(m,n)$ work and $S_{SSSP}(m,n)$ span in the parallel model, (ii) $T_{SSSP}(n,D)n^{o(1)}$, given access to an SSSP algorithm that takes $T_{SSSP}(n,D)$ rounds in $\mathsf{CONGEST}$, (iii) $Q_{SSSP}(m,n)n^{o(1)}$ quantum edge queries, given access to a non-negative-weight SSSP algorithm that takes $Q_{SSSP}(m,n)$ queries in the quantum edge query model. This work builds off the recent result of [Bernstein, Nanongkai, Wulff-Nilsen, FOCS'22], which gives a near-linear time algorithm for negative-weight SSSP in the sequential setting. Using current state-of-the-art SSSP algorithms yields randomized algorithms for negative-weight SSSP with (i) $m^{1+o(1)}$ work and $n^{1/2+o(1)}$ span in the parallel model, (ii) $(n^{2/5}D^{2/5} + \sqrt{n} + D)n^{o(1)}$ rounds in $\mathsf{CONGEST}$, (iii) $m^{1/2}n^{1/2+o(1)}$ quantum queries to the adjacency list or $n^{1.5+o(1)}$ quantum queries to the adjacency matrix. Our main technical contribution is an efficient reduction for computing a low-diameter decomposition (LDD) of directed graphs to computations of SSSP with a virtual source. Efficiently computing an LDD has heretofore only been known for undirected graphs in both the parallel and distributed models.

This paper proves that Dijkstra's shortest-path algorithm is universally optimal in both its running time and number of comparisons when combined with a sufficiently efficient heap data structure. Universal optimality is a powerful beyond-worst-case performance guarantee for graph algorithms that informally states that a single algorithm performs as well as possible for every single graph topology. We give the first application of this notion to any sequential algorithm. We design a new heap data structure with a working-set property guaranteeing that the heap takes advantage of locality in heap operations. Our heap matches the optimal (worst-case) bounds of Fibonacci heaps but also provides the beyond-worst-case guarantee that the cost of extracting the minimum element is merely logarithmic in the number of elements inserted after it instead of logarithmic in the number of all elements in the heap. This makes the extraction of recently added elements cheaper. We prove that our working-set property guarantees universal optimality for the problem of ordering vertices by their distance from the source vertex: The sequence of heap operations generated by any run of Dijkstra's algorithm on a fixed graph possesses enough locality that one can couple the number of comparisons performed by any heap with our working-set bound to the minimum number of comparisons required to solve the distance ordering problem on this graph for a worst-case choice of arc lengths.

Expander decompositions form the basis of one of the most flexible paradigms for close-to-linear-time graph algorithms. Length-constrained expander de-compositions generalize this paradigm to better work for problems with lengths, distances and costs. Roughly, an$(h,s)$-length$\phi$-expander decomposition is a small collection of length increases to a graph so that nodes within distance$h$can route flow over paths of length$hs$with congestion at most$1/\phi$. In this work, we give a close-to-linear time algorithm for computing length-constrained expander decompositions in graphs with general lengths and capacities. Notably, and unlike previous works, our algorithm allows for one to trade off off between the size of the decomposition and the length of routing paths: for any$\epsilon > 0$not too small, our algorithm computes in close-to-linear time an$(h, s)$-length$\phi$-expander decomposition of size$m\cdot\phi\cdot n^{\epsilon}$where$s$= exp(poly$(1/\epsilon)$). The key foundations of our algorithm are: (1) a simple yet powerful structural theorem which states that the union of a sequence of sparse length-constrained cuts is itself sparse and (2) new algorithms for efficiently computing sparse length-constrained flows.

We present a new distance oracle in the fully dynamic setting: given a weighted undirected graph G = (V, E) with$n$vertices undergoing both edge insertions and deletions, and an arbitrary parameter$\epsilon\in[1/\log^{c}n, 1$where$c$> 0 is a small constant, we can deterministically maintain a data structure with$O(n^{\epsilon})$worst-case update time that, given any pair of vertices (u, v), returns a$2^{\text{poly}(1/\epsilon)}$-approximate distance between$u$and$v$in poly(1/E) log log$n$query time. Our algorithm significantly advances the state-of-the-art in two aspects, both for fully dynamic algorithms and even decremental algorithms. First, no existing algorithm with worst-case update time guarantees a o($n$)-approximation while also achieving an n2-Ω(1)update and$n^{o(1)}$query time, while our algorithm offers a constant$O_{\epsilon}(1)$-approximation with$O(n^{\epsilon})$update time and$o_{\epsilon}$(log log n) query time. Second, even if amortized update time is allowed, it is the first deterministic constant-approximation algorithm with$n^{1-\Omega(1)}$update and query time. The best result in this direction is the recent deterministic distance oracle by Chuzhoy and Zhang [STOC 2023] which achieves an approxi- mation of (log log$n)^{2^{O (1 / \epsilon^3)}}$with amortized update time of$O(n^{\epsilon)}$and query time of$2^{\mathrm{p}\circ 1\mathrm{y}(1/\epsilon)}\log n$log log n. We obtain the result by dynamizing tools related to length- constrained expanders [Haeupler-Racke-Ghaffari, STOC 2022; Haeupler-Hershkowitz-Tan, FOCS 2024]. Our technique com- pletely bypasses the 40-year-old Even-Shiloach tree, which has remained the most pervasive tool in the area but is inherently amortized.

We study a new and stronger notion of fault-tolerant graph structures whose size bounds depend on the degree of the failing edge set, rather than the total number of faults. For a subset of faulty edges F ⊆ G, the faulty-degree deg(F) is the largest number of faults in F incident to any given vertex. For example, a matching F has deg(F) = 1 while |F| might be as large as n/2.

We study the consistent k-center clustering problem. In this problem, the goal is to maintain a constant factor approximate k-center solution during a sequence of n point insertions and deletions while minimizing the recourse, i.e., the number of changes made to the set of centers after each point insertion or deletion. Previous works by Lattanzi and Vassilvitskii [ICML ‘12] and Fichtenberger, Lattanzi, Norouzi-Fard, and Svensson [SODA ‘21] showed that in the incremental setting, where deletions are not allowed, one can obtain k · polylog(n)/n amortized recourse for both k-center and k-median, and demonstrated a matching lower bound. However, no algorithm for the fully dynamic setting achieves less than the trivial O(k) changes per update, which can be obtained by simply reclustering the full dataset after every update.

We introduce the concept of low-step multi-commodity flow emulators for any undirected, capacitated graph. At a high level, these emulators contain approximate multi-commodity flows whose paths contain a small number of edges, shattering the infamous flow decomposition barrier for multi-commodity flow.

This paper addresses point-to-point packet routing in undirected networks, which is the most important communication primitive in most networks. The main result proves the existence of routing tables that deterministically guarantee a polylog-competitive completion-time:

The packet routing problem asks to select routing paths that minimize the maximum edge congestion for a set of packets specified by source-destination vertex pairs. We revisit a semi-oblivious approach to this problem: each source-destination pair is assigned a small set of well-chosen predefined paths before the demand is revealed, while the sending rates along the paths can be optimally adapted to the demand. This approach has been considered in practice in network traffic engineering due to its superior robustness and performance as compared to both oblivious routing and traditional traffic engineering approaches.

This paper presents significantly improved deterministic algorithms for some of the key problems in the area of distributed graph algorithms, including network decomposition, hitting sets, and spanners. As the main ingredient in these results, we develop novel randomized distributed algorithms that we can analyze using only pairwise independence, and we can thus derandomize efficiently. As our most prominent end-result, we obtain a deterministic construction for O(log n)-color O(log n · log log log n)- strong diameter network decomposition in Õ(log3 n) rounds. This is the first construction that achieves almost log n in both parameters, and it improves on a recent line of exciting progress on deterministic distributed network decompositions [Rozhoň, Ghaffari STOC'20; Ghaffari, Grunau, Rozhoň SODA'21; Chang, Ghaffari PODC'21; Elkin, Haeupler, Rozhoň, Grunau FOCS'22].

In this work, we design an interactive coding scheme that converts any two party interactive protocol Π into another interactive protocol Π', such that even if errors are introduced during the execution of Π', the parties are able to determine what the outcome of running Π would be in an error-free setting. Importantly, our scheme preserves the space complexity of the protocol, in addition to the communication and computational complexities. Specifically, if the protocol Π has communication complexity T, computational complexity t, and space complexity s, the resulting protocol Π' is resilient to a constant ε > 0 fraction of adversarial errors, and has communication complexity approaching T as ε approaches 0, computational complexity poly(t), and space complexity

Computing routing schemes that support both high throughput and low latency is one of the core challenges of network optimization. Such routes can be formalized as h-length flows which are defined as flows whose flow paths have length at most h. Many well-studied algorithmic primitives—such as maximal and maximum length-constrained disjoint paths—are special cases of h-length flows. Likewise the optimal h-length flow is a fundamental quantity in network optimization, characterizing, up to poly-log factors, how quickly a network can accomplish numerous distributed primitives.

This paper introduces stronger notions for approximate single-source shortest-path distances and gives simple reductions to compute them from weaker standard notions of approximate distances. Strongly-approximate distances isolate, capture, and address the well-known barriers for using approximate distances algorithmically and their reductions directly address these barriers in a clean and modular manner. The reductions are model-independent and require only logO(1) n black-box approximate distance computations. They apply equally to parallel, distributed, and semi-streaming settings. Strongly (1+ε)-approximate distances are equivalent to exact distances in a (1+ε)-perturbed graph and approximately satisfy the subtractive triangle inequality. In directed graphs, this is sufficient to reduce even exact distance computation to arbitrary (1+ε)-approximate ones.

Abstract In this paper, we refine the (almost) existentially optimal distributed Laplacian solver of Forster, Goranci, Liu, Peng, Sun, and Ye (FOCS ‘21) into an (almost) universally optimal distributed Laplacian solver. Specifically, when the topology is known (i.e., the Supported-CONGEST model), we show that any Laplacian system on an n -node graph with shortcut quality $$\textrm{SQ}(G)$$ SQ ( G ) can be solved after $$n^{o(1)} \text {SQ}(G) \log (1/\epsilon )$$ n o ( 1 ) SQ ( G ) log ( 1 / ϵ ) rounds, where $$\epsilon >0$$ ϵ > 0 is the required accuracy. This almost matches our lower bound that guarantees that any correct algorithm on G requires $$\widetilde{\Omega }(\textrm{SQ}(G))$$ Ω ~ ( SQ ( G ) ) rounds, even for a crude solution with $$\epsilon \le 1/2$$ ϵ ≤ 1 / 2 . Several important implications hold in the unknown-topology (i.e., standard CONGEST) case: for excluded-minor graphs we get an almost universally optimal algorithm that terminates in $$D \cdot n^{o(1)} \log (1/\epsilon )$$ D · n o ( 1 ) log ( 1 / ϵ ) rounds, where D is the hop-diameter of the network; as well as $$n^{o(1)} \log (1/\epsilon )$$ n o ( 1 ) log ( 1 / ϵ ) -round algorithms for the case of $$\textrm{SQ}(G) \le n^{o(1)}$$ SQ ( G ) ≤ n o ( 1 ) , which holds for most networks of interest. Moreover, following a recent line of work in distributed algorithms, we consider a hybrid communication model which enhances CONGEST with limited global power in the form of the node-capacitated cli

Abstract. This paper studies linear and affine error-correcting codes for correcting synchronization errors such as insertions and deletions. We call such codes linear/affine insdel codes. Linear codes that can correct even a single deletion are limited to having an information rate at most [Formula: see text] (achieved by the trivial two fold repetition code). Previously, it was (erroneously) reported that more generally no nontrivial linear codes correcting [Formula: see text] deletions exist, i.e., that the [Formula: see text]-fold repetition codes and its rate of [Formula: see text] are basically optimal for any [Formula: see text]. We disprove this and show the existence of binary linear codes of length [Formula: see text] and rate just below [Formula: see text] capable of correcting [Formula: see text] insertions and deletions. This identifies rate [Formula: see text] as a sharp threshold for recovery from deletions for linear codes and reopens the quest for a better understanding of the capabilities of linear codes for correcting insertions/deletions. We prove novel outer bounds and existential inner bounds for the rate vs. (edit) distance trade-off of linear insdel codes. We complement our existential results with an efficient synchronization-string-based transformation that converts any asymptotically good linear code for Hamming errors into an asymptotically good linear code for insdel errors. Last, we show that the [Formula: see text]-rate limitation does not hold for affine codes by giving an explicit affine code of rate [Formula: see text] which can efficiently correct a constant fraction of insdel errors.

This paper presents new deterministic and distributed low-diameter decomposition algorithms for weighted graphs. In particular, we show that if one can efficiently compute approximate distances in a parallel or a distributed setting, one can also efficiently compute low-diameter decompositions. This consequently implies solutions to many fundamental distance based problems using a polylogarithmic number of approximate distance computations.Our low-diameter decomposition generalizes and extends the line of work starting from [RG20] to weighted graphs in a very model-independent manner. Moreover, our clustering results have additional useful properties, including strong-diameter guarantees, separation properties, restricting cluster centers to specified terminals, and more. Applications include:–The first near-linear work and polylogarithmic depth randomized and deterministic parallel algorithm for low-stretch spanning trees (LSST) with polylogarithmic stretch. Previously, the best parallel LSST algorithm required $m.n^{o(1)}$ work and $n^{o(1)}$ depth and was inherently randomized. No deterministic LSST algorithm with truly sub-quadratic work and sub-linear depth was known.–The first near-linear work and polylogarithmic depth deterministic algorithm for computing an $\ell_{1}-$embedding into polylogarithmic dimensional space with polylogarithmic distortion. The best prior deterministic algorithms for $\ell_{1}$-embeddings either require large polynomial work or are inherently sequential.Even when we apply our techniques to the classical problem of computing a ball-carving with strong-diameter $O(\log^{2}n)$ in an unweighted graph, our new clustering algorithm still leads to an improvement in round complexity from $O(\log^{10}n)$ rounds [CG21] to $O(\log^{4}n)$.

This paper presents general bounds on the highest achievable rate for list-decodable insertion-deletion codes. In particular, we give novel outer and inner bounds for the highest achievable communication rate of any insertion-deletion code that can be list-decoded from any γ fraction of insertions and any δ fraction of deletions. Our bounds simultaneously generalize the known bounds for the previously studied special cases of insertion-only, deletion-only, and zero-rate and correct other bounds that had been reported for the general case.

This paper refines the distributed Laplacian solver recently developed by Forster, Goranci, Liu, Peng, Sun, and Ye (FOCS '21) via the Ghaffari-Haeupler framework (SODA '16) of low-congestion shortcuts. Specifically, if ε > 0 is the error of the Laplacian solver, we obtain two main results.

We provide universally-optimal distributed graph algorithms for (1+∊)-approximate shortest path problems including shortest-path-tree and transshipment. The universal optimality of our algorithms guarantees that, on any n-node network G, our algorithm completes in T · no(1) rounds whenever a T-round algorithm exists for G. This includes D · no(1)-round algorithms for any planar or excluded-minor network. Our algorithms never require more than rounds, resulting in the first sub-linear-round distributed algorithm for transshipment. The key technical contribution leading to these results is the first efficient no(1)-competitive linear ℓ1-oblivious routing operator that does not require the use of ℓ1-embeddings. Our construction is simple, solely based on low-diameter decompositions, and—in contrast to all known constructions—directly produces an oblivious flow instead of just an approximation of the optimal flow cost. This also has the benefit of simplifying the interaction with Sherman's multiplicative weight framework [SODA'17] in the distributed setting and its subsequent rounding procedures.

This paper presents efficient distributed algorithms for a number of fundamental problems in the area of graph sparsification:We provide the first deterministic distributed algorithm that computes an ultra-sparse spanner in polylog(n) rounds in weighted graphs. Concretely, our algorithm outputs a spanning subgraph with only n + o (n) edges in which the pairwise distances are stretched by a factor of at most O(logn · 2O(log* n) ).

Given a Boolean circuit C, we wish to convert it to a circuit C′ that computes the same function as C even if some of its gates suffer from adversarial short circuit errors, i.e., their output is replaced by the value of one of their inputs. Can we design such a resilient circuit C′ whose size is roughly comparable to that of C? Prior work gave a positive answer for the special case where C is a formula.

This paper studies the fundamental task of establishing routing paths in distributed networks. We prove the existence of compact routing tables that store in each network-node few simple forwarding rules tracing out hop-constrained and oblivious routing paths for any pair of nodes. For any collection of pairs the congestion of these paths is almost-optimal, i.e., competitive with the globally optimal solution up to a sub-polynomial factor.

This paper presents near-optimal deterministic parallel and distributed algorithms for computing (1+eps)-approximate single-source shortest paths in any undirected weighted graph.

We study the store-and-forward packet routing problem for simultaneous multicasts, in which multiple packets have to be forwarded along given trees as fast as possible. This is a natural generalization of the seminal work of Leighton, Maggs and Rao, which solved this problem for unicasts, i.e. the case where all trees are paths. They showed the existence of asymptotically optimal O(C + D)-length schedules, where the congestion C is the maximum number of packets sent over an edge and the dilation D is the maximum depth of a tree. This improves over the trivial O(CD) length schedules. We prove a lower bound for multicasts, which shows that there do not always exist schedules of non-trivial length, o(CD). On the positive side, we construct O(C+D+log² n)-length schedules in any n-node network. These schedules are near-optimal, since our lower bound shows that this length cannot be improved to O(C+D) + o(log n).

We prove that any n-node graph G with diameter D admits shortcuts with congestion O(δ D log n) and dilation O(δ D), where δ is the maximum edge-density of any minor of G. Our proof is simple and constructive with a tildeΘ (δ D)-round1 distributed construction algorithm. Our results are tight up to logarithmic factors and generalize, simplify, unify, and strengthen several prior results. For example, for graphs excluding a fixed minor, i.e., graphs with constant δ, only a Õ (D2) bound was known based on a very technical proof that relies on the Robertson-Seymour Graph Structure Theorem.

This paper studies linear and affine error-correcting codes for correcting synchronization errors such as insertions and deletions. We call such codes linear/affine insdel codes. Linear codes that can correct even a single deletion are limited to have information rate at most 1/2 (achieved by the trivial 2-fold repetition code). Previously it was (erroneously) reported that more generally no non-trivial linear codes correcting k deletions exist, i.e., that the (k + 1)-fold repetition codes and its rate of 1/(k + 1) are basically optimal for any k. We disprove this and show the existence of binary linear codes of length n and rate just below 1/2 capable of correcting Ω(n) insertions and deletions. This identifies rate 1/2 as a sharp threshold for recovery from deletions for linear codes, and reopens the quest for a better understanding of the capabilities of linear codes for correcting insertions/deletions. We prove novel outer bounds and existential inner bounds for the rate vs. (edit) distance trade-off of linear insdel codes. We complement our existential results with an efficient synchronization-string-based transformation that converts any asymptotically-good linear code for Hamming errors into an asymptotically-good linear code for insdel errors. Lastly we show that the ½-rate limitation does not hold for affine codes by giving an explicit affine code of rate 1 – ∊ which can efficiently correct a constant fraction of insdel errors.

We present a randomized parallel algorithm, in the Exclusive-Read Exclusive-Write (EREW) PRAM model, that computes a Maximal Independent Set (MIS) in O(log n) time and using O(m log2 n) work, with high probability. Thus, MIS ∊ RNC1. This time complexity is optimal and it improves on the celebrated O(log2 n) time algorithms of Luby [STOC'85] and Alon, Babai, and Itai [JALG'86], which had remained the state of the art for the past 35 years.

We prove the existence of an oblivious routing scheme that is poly(logn)-competitive in terms of (congestion + dilation), thus resolving a well-known question in oblivious routing.

Network design problems aim to compute low-cost structures such as routes, trees and subgraphs. Often, it is natural and desirable to require that these structures have small hop length or hop diameter. Unfortunately, optimization problems with hop constraints are much harder and less well understood than their hop-unconstrained counterparts. A significant algorithmic barrier in this setting is the fact that hop-constrained distances in graphs are very far from being a metric.

Many distributed optimization algorithms achieve existentially-optimal running times, meaning that there exists some pathological worst-case topology on which no algorithm can do better. Still, most networks of interest allow for exponentially faster algorithms. This motivates two questions:

Many distributed optimization algorithms achieve an existentially-optimal round complexity (of (Õ(√n + D)), i.e., there exists some pathological worst-case topology on which no algorithm can be faster. However, most networks of interest allow for exponentially faster algorithms. This motivates two questions: - What network topology parameters determine the complexity of distributed optimization? - Are there universally-optimal algorithms that are as fast as possible on every single topology? This talk provides an overview over the freshly-completed 6-year program that resolves these 25-year-old open problems for a wide class of global network optimization problems including MST, (1+ε)-min cut, various approximate shortest path problems, sub-graph connectivity, etc. We provide several equivalent graph parameters that are tight universal lower bounds for the above problems, fully characterizing their inherent complexity. We also give the first universally-optimal algorithms approximately achieving this complexity on every topology. The quest for universally-optimal distributed algorithms required novel techniques that also answer fundamental (open) questions in seemingly unrelated fields, such as, network information theory, approximation algorithms, (oblivious) packet routing, (algorithmic & topological) graph theory, and metric embeddings. Generally, the problems addressed in these fields explicitly or implicitly ask to jointly optimize 𝓁_∞ & 𝓁₁ parameters such as congestion & dilation, communication rate & delay, capacities & diameters of subnetworks, or the makespan of packet routings. In particular, results obtained on the way include the following firsts: (Congestion+Dilation)-Competitive Oblivious Routing, Network Coding Gaps for Completion-Times, Hop-Constrained Expanders & Expander Decompositions, Bi-Criteria (Online / Demand-Robust) Approximation Algorithms for many Diameter-Constrained Network Design Problems (e.g., (Group) Steiner Tree/Forest), Makespan-Competitive (Compact and Distributed) Routing Tables, and (Probabilistic) Tree Embeddings for Hop-Constrained Distances. (Joint work with M. Ghaffari, G. Zuzic, D.E. Hershkowitz, D. Wajc, J. Li, H. Raecke, T. Izumi)

We introduce synchronization strings , which provide a novel way to efficiently deal with synchronization errors , i.e., insertions and deletions. Synchronization errors are strictly more general and much harder to cope with than more commonly considered Hamming-type errors , i.e., symbol substitutions and erasures. For every ε > 0, synchronization strings allow us to index a sequence with an ε -O(1) -size alphabet, such that one can efficiently transform k synchronization errors into (1 + ε)k Hamming-type errors . This powerful new technique has many applications. In this article, we focus on designing insdel codes , i.e., error correcting block codes (ECCs) for insertion-deletion channels. While ECCs for both Hamming-type errors and synchronization errors have been intensely studied, the latter has largely resisted progress. As Mitzenmacher puts it in his 2009 survey [30]: “ Channels with synchronization errors...are simply not adequately understood by current theory. Given the near-complete knowledge, we have for channels with erasures and errors...our lack of understanding about channels with synchronization errors is truly remarkable. ” Indeed, it took until 1999 for the first insdel codes with constant rate, constant distance, and constant alphabet size to be constructed and only since 2016 are there constructions of constant rate insdel codes for asymptotically large noise rates. Even in the asymptotically large or small noise regimes, these codes are polynomially far from the optimal rate-distance tradeoff. This makes the understanding of insdel codes up to this work equivalent to what was known for regular ECCs after Forney introduced concatenated codes in his doctoral thesis 50 years ago. A straightforward application of our synchronization strings-based indexing method gives a simple black-box construction that transforms any ECC into an equally efficient insdel code with only a small increase in the alphabet size. This instantly transfers much of the highly developed understanding for regular ECCs into the realm of insdel codes. Most notably, for the complete noise spectrum, we obtain efficient “near-MDS” insdel codes, which get arbitrarily close to the optimal rate-distance tradeoff given by the Singleton bound. In particular, for any δ ∈ (0,1) and ε > 0, we give a family of insdel codes achieving a rate of 1 - δ - ε over a constant-size alphabet that efficiently corrects a δ fraction of insertions or deletions.

We give a complete answer to the following basic question: “What is the maximal fraction of deletions or insertions tolerable by$q$-ary list-decodable codes with non-vanishing information rate?” This question has been open even for binary codes, including the restriction to the binary insertion-only setting, where the best-known result was that a$\gamma \leq 0.707$fraction of insertions is tolerable by some binary code family. For any desired$\varepsilon > 0$, we construct a family of binary codes of positive rate which can be efficiently list-decoded from any combination of$\gamma $fraction of insertions and$\delta $fraction of deletions as long as$\gamma + 2\delta \leq 1 - \varepsilon $. On the other hand, for any$\gamma, \delta $with$\gamma + 2\delta = 1$list-decoding is impossible. Our result thus precisely characterizes the feasibility region of binary list-decodable codes for insertions and deletions. We further generalize our result to codes over any finite alphabet of size$q$. Surprisingly, our work reveals that the feasibility region for$q>2$isnotthe natural generalization of the binary bound above. We provide tight upper and lower bounds that precisely pin down the feasibility region, which turns out to have a$(q-1)$-piece-wise linear boundary whose$q$corner-points lie on a quadratic curve. The main technical work in our results is proving the existence of code families of sufficiently largesizewith good list-decoding properties for any combination of$\delta,\gamma $within the claimed feasibility region. We achieve this via an intricate analysis of codes introduced by [Bukh and Ma, 2014]. Finally, we give a simple yet powerful concatenation scheme for list-decodable insertion-deletion codes which transforms any such (non-efficient) code family (with vanishing information rate) into an efficiently decodable code family with constant rate.

Already in the 1960s, Levenshtein and others studied error-correcting codes that protect against synchronization errors, such as symbol insertions and deletions. However, despite significant efforts, progress on designing such codes has been lagging until recently, particularly compared to the detailed understanding of error-correcting codes for symbol substitution or erasure errors. This paper surveys the recent progress in designing efficient error-correcting codes over finite alphabets that can correct a constant fraction of worst-case insertions and deletions. Most state-of-the-art results for such codes rely on synchronization strings, simple yet powerful pseudo-random objects that have proven to be very effective solutions for coping with synchronization errors in various settings. This survey also includes an overview of what is known about synchronization strings and discusses communication settings related to error-correcting codes in which synchronization strings have been applied.

We study network coding gaps for the problem of makespan minimization of multiple unicasts. In this problem distinct packets at different nodes in a network need to be delivered to a destination specific to each packet, as fast as possible. The network coding gap specifies how much coding packets together in a network can help compared to the more natural approach of routing. While makespan minimization using routing has been intensely studied for the multiple unicasts problem, no bounds on network coding gaps for this problem are known. We develop new techniques which allow us to upper bound the network coding gap for the makespan of k unicasts, proving this gap is at most polylogarithmic in k. Complementing this result, we show there exist instances of k unicasts for which this coding gap is polylogarithmic in k. Our results also hold for average completion time, and more generally any lp norm of completion times.

Data aggregation is a fundamental primitive in distributed computing wherein a network computes a function of every nodes' input. However, while compute time is non-negligible in modern systems, standard models of distributed computing do not take compute time into account. Rather, most distributed models of computation only explicitly consider communication time. In this paper, we introduce a model of distributed computation that considers both computation and communication so as to give a theoretical treatment of data aggregation. We study both the structure of and how to compute the fastest data aggregation schedule in this model. As our first result, we give a polynomial-time algorithm that computes the optimal schedule when the input network is a complete graph. Moreover, since one may want to aggregate data over a pre-existing network, we also study data aggregation scheduling on arbitrary graphs. We demonstrate that this problem on arbitrary graphs is hard to approximate within a multiplicative 1.5 factor. Finally, we give an O(log n ⋅ log(OPT/t_m))-approximation algorithm for this problem on arbitrary graphs, where n is the number of nodes and OPT is the length of the optimal schedule.

We give the first communication-optimal document exchange protocol. For any n and kε, produces a summary of size O(klog2k + k log n), and succeeds with probability 1-(k log n)-O(1). We also give an efficient derandomized document exchange protocol with summary size O(k log2n/k). This improves, for any k, over a deterministic document exchange protocol by Belazzougui with summary size O(k2+ k log2n). Our deterministic document exchange directly provides new efficient systematic error correcting codes for insertions and deletions. These (binary) codes correct any δ fraction of adversarial insertions/deletions while having a rate of 1 - O(δ log21/δ) and improve over the codes of Guruswami and Li and Haeupler, Shahrasbi and Vitercik which have rate 1 - Θ (√δ logO(1)1/ε).

Synchronization strings are recently introduced by Haeupler and Shahrasbi [1] in the study of codes for correcting insertion and deletion errors (insdel codes). A synchronization string is an encoding of the indices of the symbols in a string, and together with an appropriate decoding algorithm it can transform insertion and deletion errors into standard symbol erasures and corruptions. This reduces the problem of constructing insdel codes to the problem of constructing standard error correcting codes, which is much better understood. Besides this, synchronization strings are also useful in other applications such as synchronization sequences and interactive coding schemes. For all such applications, synchronization strings are desired to be over alphabets that are as small as possible, since a larger alphabet size corresponds to more redundant information added. Haeupler and Shahrasbi [1] showed that for any parameter ε > 0, synchronization strings of arbitrary length exist over an alphabet whose size depends only on ε. Specifically, [1] obtained an alphabet size of O(ε−4), which left an open question on where the minimal size of such alphabets lies between Ω(ε−1) and O(ε−4). In this work, we partially bridge this gap by providing an improved lower bound of Ω (ε−3/2), and an improved upper bound of O (ε−2). We also provide fast explicit constructions of synchronization strings over small alphabets. Further, along the lines of previous work on similar combinatorial objects, we study the extremal question of the smallest possible alphabet size over which synchronization strings can exist for some constant ε < 1. We show that one can construct ε-synchronization strings over alphabets of size four while no such string exists over binary alphabets. This reduces the extremal question to whether synchronization strings exist over ternary alphabets.

Motivated by emerging memory technologies and the increasing importance of energy and bandwidth, we study the Writeback-Aware Caching Problem. This problem modifies the caching problem by explicitly accounting for the cost of writing data to memory. In the offline setting with maximum writeback cost ømega > 0, we show that the writeback-oblivious optimal policy is only (ømega+1)-competitive for writeback-aware caching, and that writeback-aware caching is NP-complete and Max-SNP hard. In the online setting, we present a deterministic online replacement policy, called Writeback-Aware Landlord, and show that it obtains the optimal competitive ratio. Finally, we perform an experimental study on real-world traces which shows that Writeback-Aware Landlord outperforms state-of-the-art cache replacement policies when writebacks are costly.

We introduce fast-decodable indexing schemes for edit distance which can be used to speed up edit distance computations to near-linear time if one of the strings is indexed by an indexing string I. In particular, for every length n and every ε >0, one can in near linear time construct a string I ∈ Σ′n with |Σ′| = Oε(1), such that, indexing any string S ∈ Σn, symbol-by-symbol, with I results in a string S′ ∈ Σ″n where Σ″ = Σ × Σ′ for which edit distance computations are easy, i.e., one can compute a (1+ε)-approximation of the edit distance between S′ and any other string in O(n (logn)) time.

The radio network model is a well-studied model of wireless, multi-hop networks. However, radio networks make the strong assumption that messages are delivered deterministically. The recently introduced noisy radio network model relaxes this assumption by dropping messages independently at random. In this work we quantify the relative computational power of noisy radio networks and classic radio networks. In particular, given a non-adaptive protocol for a fixed radio network we show how to reliably simulate this protocol if noise is introduced with a multiplicative cost of $\mathrm{poly}(\log Δ, \log \log n)$ rounds where $n$ is the number nodes in the network and $Δ$ is the max degree. Moreover, we demonstrate that, even if the simulated protocol is not non-adaptive, it can be simulated with a multiplicative $O(Δ\log ^2 Δ)$ cost in the number of rounds. Lastly, we argue that simulations with a multiplicative overhead of $o(\log Δ)$ are unlikely to exist by proving that an $Ω(\log Δ)$ multiplicative round overhead is necessary under certain natural assumptions.

We consider the task of multiparty computation performed over networks in the presence of random noise. Given an n -party protocol that takes R rounds assuming noiseless communication, the goal is to find a coding scheme that takes \(R'\) rounds and computes the same function with high probability even when the communication is noisy, while maintaining a constant asymptotic rate , i.e., while keeping \(\liminf _{n,R\rightarrow \infty } R/R'\) positive. Rajagopalan and Schulman (STOC ’94) were the first to consider this question, and provided a coding scheme with rate \(O(1/\log (d+1))\) , where d is the maximal degree in the network. While that scheme provides a constant rate coding for many practical situations, in the worst case, e.g., when the network is a complete graph, the rate is \(O(1/\log n)\) , which tends to 0 as n tends to infinity. We revisit this question and provide an efficient coding scheme with a constant rate for the interesting case of fully connected networks. We furthermore extend the result and show that if a ( d -regular) network has mixing time m , then there exists an efficient coding scheme with rate \(O(1/m^3\log m)\) . This implies a constant rate coding scheme for any n -party protocol over a d -regular network with a constant mixing time, and in particular for random graphs with n vertices and degrees \(n^{\varOmega (1)}\) .

The noisy broadcast model was first studied by [Gallager, 1988] where an n-character input is distributed among n processors, so that each processor receives one input bit. Computation proceeds in rounds, where in each round each processor broadcasts a single character, and each reception is corrupted independently at random with some probability p. [Gallager, 1988] gave an algorithm for all processors to learn the input in O(log log n) rounds with high probability. Later, a matching lower bound of Omega(log log n) was given by [Goyal et al., 2008]. We study a relaxed version of this model where each reception is erased and replaced with a `?' independently with probability p, so the processors have knowledge of whether a bit has been corrupted. In this relaxed model, we break past the lower bound of [Goyal et al., 2008] and obtain an O(log^* n)-round algorithm for all processors to learn the input with high probability. We also show an O(1)-round algorithm for the same problem when the alphabet size is Omega(poly(n)).

We study codes that are list-decodable under insertions and deletions ("insdel codes"). Specifically, we consider the setting where, given a codeword x of length n over some finite alphabet Sigma of size q, delta * n codeword symbols may be adversarially deleted and gamma * n symbols may be adversarially inserted to yield a corrupted word w. A code is said to be list-decodable if there is an (efficient) algorithm that, given w, reports a small list of codewords that include the original codeword x. Given delta and gamma we study what is the rate R for which there exists a constant q and list size L such that there exist codes of rate R correcting delta-fraction insertions and gamma-fraction deletions while reporting lists of size at most L. Using the concept of synchronization strings, introduced by the first two authors [Proc. STOC 2017], we show some surprising results. We show that for every 0 <= delta < 1, every 0 <= gamma < infty and every epsilon > 0 there exist codes of rate 1 - delta - epsilon and constant alphabet (so q = O_{delta,gamma,epsilon}(1)) and sub-logarithmic list sizes. Furthermore, our codes are accompanied by efficient (polynomial time) decoding algorithms. We stress that the fraction of insertions can be arbitrarily large (more than 100%), and the rate is independent of this parameter. We also prove several tight bounds on the parameters of list-decodable insdel codes. In particular, we show that the alphabet size of insdel codes needs to be exponentially large in epsilon^{-1}, where epsilon is the gap to capacity above. Our result even applies to settings where the unique-decoding capacity equals the list-decoding capacity and when it does so, it shows that the alphabet size needs to be exponentially large in the gap to capacity. This is sharp contrast to the Hamming error model where alphabet size polynomial in epsilon^{-1} suffices for unique decoding. This lower bound also shows that the exponential dependence on the alphabet size in previous works that constructed insdel codes is actually necessary! Our result sheds light on the remarkable asymmetry between the impact of insertions and deletions from the point of view of error-correction: Whereas deletions cost in the rate of the code, insertion costs are borne by the adversary and not the code! Our results also highlight the dominance of the model of insertions and deletions over the Hamming model: A Hamming error is equal to one insertion and one deletion (at the same location). Thus the effect of delta-fraction Hamming errors can be simulated by delta-fraction of deletions and delta-fraction of insertions - but insdel codes can deal with much more insertions without loss in rate (though at the price of higher alphabet size).

We present many new results related to reliable (interactive) communication over insertion-deletion channels. Synchronization errors, such as insertions and deletions, strictly generalize the usual symbol corruption errors and are much harder to protect against. We show how to hide the complications of synchronization errors in many applications by introducing very general channel simulations which efficiently transform an insertion-deletion channel into a regular symbol corruption channel with an error rate larger by a constant factor and a slightly smaller alphabet. We utilize and generalize synchronization string based methods which were recently introduced as a tool to design essentially optimal error correcting codes for insertion-deletion channels. Our channel simulations depend on the fact that, at the cost of increasing the error rate by a constant factor, synchronization strings can be decoded in a streaming manner that preserves linearity of time. Interestingly, we provide a lower bound showing that this constant factor cannot be improved to 1+epsilon, in contrast to what is achievable for error correcting codes. Our channel simulations drastically and cleanly generalize the applicability of synchronization strings. We provide new interactive coding schemes which simulate any interactive two-party protocol over an insertion-deletion channel. Our results improve over the interactive coding schemes of Braverman et al. [TransInf `17] and Sherstov and Wu [FOCS `17] which achieve a small constant rate and require exponential time computations with respect to computational and communication complexities. We provide the first computationally efficient interactive coding schemes for synchronization errors, the first coding scheme with a rate approaching one for small noise rates, and also the first coding scheme that works over arbitrarily small alphabet sizes. We also show tight connections between synchronization strings and edit-distance tree codes which allow us to transfer results from tree codes directly to edit-distance tree codes. Finally, using on our channel simulations, we provide an explicit low-rate binary insertion-deletion code that improves over the state-of-the-art codes by Guruswami and Wang [TransInf `17] in terms of rate-distance trade-off.

We consider the problem of making distributed computations robust to noise, in particular to worst-case (adversarial) corruptions of messages. We give a general distributed interactive coding scheme which simulates any asynchronous distributed protocol while tolerating a maximal corruption level of \Theta(1/n)-fraction of all messages. Our noise tolerance is optimal and is obtained with only a moderate overhead in the number of messages. Our result is the first fully distributed interactive coding scheme in which the topology of the communication network is not known in advance. Prior work required either a coordinating node to be connected to all other nodes in the network or assumed a synchronous network in which all nodes already know the complete topology of the network. Overcoming this more realistic setting of an unknown topology leads to intriguing distributed problems, in which nodes try to learn sufficient information about the network topology in order to perform efficient coding and routing operations for coping with the noise. What makes these problems hard is that these topology exploration computations themselves must already be robust to noise.

Distributed graph algorithms that separately optimize for either the number of rounds used or the total number of messages sent have been studied extensively. However, algorithms simultaneously efficient with respect to both measures have been elusive. For example, only very recently was it shown that for Minimum Spanning Tree (MST), an optimal message and round complexity is achievable (up to polylog terms) by a single algorithm in the CONGEST model of communication.

This paper gives drastically faster gossip algorithms to compute exact and approximate quantiles.

This paper makes progress on the problem of explicitly constructing a binary tree code with constant distance and constant alphabet size.

This paper gives new results for synchronization strings, a powerful combinatorial object introduced by [Haeupler, Shahrasbi; STOC’17] that allows to efficiently deal with insertions and deletions in various communication problems:

Many fundamental problems in shared-memory distributed computing, including mutual exclusion [James E. Burns and Nancy A. Lynch, 1993], consensus [Leqi Zhu, 2016], and implementations of many sequential objects [Prasad Jayanti et al., 2000], are known to require linear space in the worst case. However, these lower bounds all work by constructing particular executions for any given algorithm that may be both very long and very improbable. The significance of these bounds is justified by an assumption that any space that is used in some execution must be allocated for all executions. This assumption is not consistent with the storage allocation mechanisms of actual practical systems. We consider the consequences of adopting a per-execution approach to space complexity, where an object only counts toward the space complexity of an execution if it is used in that execution. This allows us to show that many known randomized algorithms for fundamental problems in shared-memory distributed computing have expected space complexity much lower than the worst-case lower bounds, and that many algorithms that are adaptive in time complexity can also be made adaptive in space complexity. For the specific problem of mutual exclusion, we develop a new algorithm that illustrates an apparent trade-off between low expected space complexity and low expected RMR complexity. Whether this trade-off is necessary is an open problem. For some applications, it may be helpful to pay only for objects that are updated, as opposed to those that are merely read. We give a data structure that requires no space to represent objects that are not updated at the cost of a small overhead on those that are.

A long series of recent results and breakthroughs have led to faster and better distributed approximation algorithms for single source shortest paths (SSSP) and related problems in the CONGEST model. The runtime of all these algorithms, however, is Omega~(sqrt{n}), regardless of the network topology, even on nice networks with a (poly)logarithmic network diameter D. While this is known to be necessary for some pathological networks, most topologies of interest are arguably not of this type. We give the first distributed approximation algorithms for shortest paths problems that adjust to the topology they are run on, thus achieving significantly faster running times on many topologies of interest. The running time of our algorithms depends on and is close to Q, where Q is the quality of the best shortcut that exists for the given topology. While Q = Theta~(sqrt{n} + D) for pathological worst-case topologies, many topologies of interest have Q = Theta~(D), which results in near instance optimal running times for our algorithm, given the trivial Omega(D) lower bound. The problems we consider are as follows: - an approximate shortest path tree and SSSP distances, - a polylogarithmic size distance label for every node such that from the labels of any two nodes alone one can determine their distance (approximately), and - an (approximately) optimal flow for the transshipment problem. Our algorithms have a tunable tradeoff between running time and approximation ratio. Our fastest algorithms have an arbitrarily good polynomial approximation guarantee and an essentially optimal O~(Q) running time. On the other end of the spectrum, we achieve polylogarithmic approximations in O~(Q * n^epsilon) rounds for any epsilon > 0. It seems likely that eventually, our non-trivial approximation algorithms for the SSSP tree and transshipment problem can be bootstrapped to give fast Q * 2^O(sqrt{log n log log n}) round (1+epsilon)-approximation algorithms using a recent result by Becker et al.

We show an explicit (that is, efficient and deterministic) capacity approaching interactive coding scheme that simulates any interactive protocol under random errors with nearly optimal communication rate. Specifically, over the binary symmetric channel with crossover probability ϵ, our coding scheme achieves a communication rate of 1- O(√/H(ϵ)), together with negligible exp(-Ω(ϵ4n/logn)) failure probability (over the randomness of the channel). A rate of 1 - Θ(√/H(ϵ)) is likely asymptotically optimal as a result of Kol and Raz (2013) suggests. Prior to this paper, such a communication rate was achievable only using randomized coding schemes [Kol and Raz (2013); Hauepler (2014)].

The widely-studied radio network model [Chlamtac and Kutten, 1985] is a graph-based description that captures the inherent impact of collisions in wireless communication. In this model, the strong assumption is made that node v receives a message from a neighbor if and only if exactly one of its neighbors broadcasts. We relax this assumption by introducing a new noisy radio network model in which random faults occur at senders or receivers. Specifically, for a constant noise parameter p ∈ [0,1), either every sender has probability p of transmitting noise or every receiver of a single transmission in its neighborhood has probability p of receiving noise.

The Lovász Local Lemma (LLL) is a cornerstone principle in the probabilistic method of combinatorics, and a seminal algorithm of Moser & Tardos (2010) provides an efficient randomized algorithm to implement it. This algorithm can be parallelized to give an algorithm that uses polynomially many processors and runs in O(log3 n) time, stemming from O(log n) adaptive computations of a maximal independent set (MIS). Chung et al. (2014) developed faster local and parallel algorithms, potentially running in time O (log2 n), but these algorithms work under significantly more stringent conditions than the LLL. We give a new parallel algorithm that works under essentially the same conditions as the original algorithm of Moser & Tardos but uses only a single MIS computation, thus running in O(log2 n) time. This conceptually new algorithm also gives a clean combinatorial description of a satisfying assignment which might be of independent interest. Our techniques extend to the deterministic LLL algorithm given by Chandrasekaran et al. (2013) leading to an NC-algorithm running in time O(log2 n) as well. We also provide improved bounds on the runtimes of the sequential and parallel resampling-based algorithms originally developed by Moser & Tardos. Our bounds extend to any problem instance in which the tighter Shearer LLL criterion is satisfied. We also improve on the analysis of Kolipaka & Szegedy (2011) to give tighter concentration results.

We study the communication rate of coding schemes for interactive communication that transform any two-party interactive protocol into a protocol that is robust to noise. Recently, Haeupler [11] showed that if an ∊ > 0 fraction of transmissions are corrupted, adversarially or randomly, then it is possible to achieve a communication rate of Furthermore, Haeupler conjectured that this rate is optimal for general input protocols. This stands in contrast to the classical setting of one-way communication in which error-correcting codes are known to achieve an optimal communication rate of 1 In this work, we show that the quadratically smaller rate loss of the one-way setting can also be achieved in interactive coding schemes for a very natural class of input protocols. We introduce the notion of average message length, or the average number of bits a party sends before receiving a reply, as a natural parameter for measuring the level of interactivity in a protocol. Moreover, we show that any protocol with average message length ℓ = Ω(poly(1/∊)) can be simulated by a protocol with optimal communication rate 1 — Θ(Η(∊)) over an oblivious adversarial channel with error fraction e. Furthermore, under the additional assumption of access to public shared randomness, the optimal communication rate is achieved ratelessly, i.e., the communication rate adapts automatically to the actual error rate e without having to specify it in advance. This shows that the capacity gap between one-way and interactive communication can be bridged even for very small (constant in e) average message lengths, which are likely to be found in many applications.

We introduce synchronization strings, which provide a novel way of efficiently dealing with synchronization errors, i.e., insertions and deletions. Synchronization errors are strictly more general and much harder to deal with than more commonly considered half-errors, i.e., symbol corruptions and erasures. For every ε > 0, synchronization strings allow to index a sequence with an ε-O(1) size alphabet such that one can efficiently transform k synchronization errors into (1 + ε)k half-errors. This powerful new technique has many applications. In this paper we focus on designing insdel codes, i.e., error correcting block codes (ECCs) for insertion deletion channels.

In this paper, we study how a collection of interconnected nodes can efficiently perform a global computation in the $\mathcal{GOSSIP}$ model of communication. In this model nodes do not know the global topology of the network and may only initiate contact with a single neighbor in each round. This contrasts with the much less restrictive $\mathcal{LOCAL}$ model, where a node may simultaneously communicate with all of its neighbors in a single round. A basic question in this setting is how many rounds of communication are required for the information dissemination problem, in which each node has some piece of information and is required to collect all others. In the $\mathcal{LOCAL}$ model this is quite simple: each node broadcasts all of its information in each round, and the number of rounds required will be equal to the diameter of the underlying communication graph. In the $\mathcal{GOSSIP}$ model, each node must independently choose a single neighbor to contact, and the lack of global information makes it difficult to make any sort of principled choice. As such, researchers have focused on the uniform gossip algorithm, in which each node independently selects a neighbor uniformly at random. When the graph is well-connected, this works quite well. In a string of beautiful papers, researchers proved a sequence of successively stronger bounds on the number of rounds required in terms of the conductance $\phi$ and graph size $n$, culminating in a bound of $\Theta(\phi^{-1} \log n)$. In this paper, we give the first protocol that works efficiently on any topology. In particular we give an algorithm that solves the information dissemination problem in at most $O(D+\text{polylog}{(n)})$ rounds in a network of diameter $D$, with no dependence on the conductance. This is at most an additive polylogarithmic factor from the trivial lower bound of $D$. In fact, we prove that something stronger is true: any algorithm that requires $T$ rounds in the $\mathcal{LOCAL}$ model can be simulated in $O(T +\mathrm{polylog}(n))$ rounds in the $\mathcal{GOSSIP}$ model. We thus prove that these two models of distributed computation are equivalent up to an additive polylogarithmic term.

We consider interactive communication performed over two types of noisy channels: binary error channels with noiseless feedback and binary erasure channels. In both cases, the noise model is adversarial. Assuming at most $\varepsilon$-fraction of the bits can be corrupted, we show coding schemes that simulate any alternating interactive protocol with rate $1-\Theta(H(\varepsilon))$. All our simulations are simple, randomized, and computationally efficient. The rates of our coding schemes stand in contrast to the interactive communication rates supported by random or adversarial error channels without feedback, for which the best known coding schemes achieve rates of $1-\Theta(\sqrt{\varepsilon})$ and $1-\Theta(\sqrt{\varepsilon \log \log 1/\varepsilon})$, respectively. As these rates are conjectured to be optimal, our result implies a large asymptotic gap between interactive communication rates over noisy channels with and without feedback. Such a gap has no equivalent in the standard one-way communication setting.

A fundamental result by Karger [10] states that for any λ-edge-connected graph with n nodes, independently sampling each edge with probability p = Ω(log ( n )/λ) results in a graph that has edge connectivity Ω(λ p ), with high probability. This article proves the analogous result for vertex connectivity, when either vertices or edges are sampled. We show that for any k -vertex-connected graph G with n nodes, if each node is independently sampled with probability p =Ω(√log( n )/ k ), then the subgraph induced by the sampled nodes has vertex connectivity Ω( kp 2 ), with high probability. If edges are sampled with probability p = Ω(log ( n )/ k ), then the sampled subgraph has vertex connectivity Ω( kp ), with high probability. Both bounds are existentially optimal.

The Lovász Local Lemma (LLL) is a cornerstone principle in the probabilistic method of combinatorics, and a seminal algorithm of Moser and Tardos (2010) provides an efficient randomized algorithm to implement it. This can be parallelized to give an algorithm that uses polynomially many processors and runs in O (log 3 n ) time on an EREW PRAM, stemming from O (log n ) adaptive computations of a maximal independent set (MIS). Chung et al. (2014) developed faster local and parallel algorithms, potentially running in time O (log 2 n ), but these algorithms require more stringent conditions than the LLL. We give a new parallel algorithm that works under essentially the same conditions as the original algorithm of Moser and Tardos but uses only a single MIS computation, thus running in O (log 2 n ) time on an EREW PRAM. This can be derandomized to give an NC algorithm running in time O (log 2 n ) as well, speeding up a previous NC LLL algorithm of Chandrasekaran et al. (2013). We also provide improved and tighter bounds on the runtimes of the sequential and parallel resampling-based algorithms originally developed by Moser and Tardos. These apply to any problem instance in which the tighter Shearer LLL criterion is satisfied.

This paper presents the first (non-trivial) distributed planar embedding algorithm. We consider this a crucial first step in a broader program to design efficient distributed algorithms for planar networks. We work in the standard distributed model in which nodes can send an O(log n)-bit message to each of their neighbors per round. In a planar network, with n nodes and diameter D, our deterministic planar embedding algorithm uses O(D dot min{log n, D) rounds to compute a combinatorial planar embedding, which consists of each node knowing the clockwise order of its incident edges in a fixed planar drawing. The complexity of our algorithm is near-optimal and matches the trivial lower bound of Omega(D) up to a log n factor. No algorithm outperforming the trivial round complexity of O(n) was known prior to this work.

Distributed optimization algorithms are frequently faced with solving sub-problems on disjoint connected parts of a network. Unfortunately, the diameter of these parts can be significantly larger than the diameter of the underlying network, leading to slow running times. Recent work by [Ghaffari and Hauepler; SODA'16] showed that this phenomenon can be seen as the broad underlying reason for the pervasive Omega(√n + D) lower bounds that apply to most optimization problems in the CONGEST model. On the positive side, this work also introduced low-congestion shortcuts as an elegant solution to circumvent this problem in certain topologies of interest. Particularly, they showed that there exist good shortcuts for any planar network and more generally any bounded genus network. This directly leads to fast O(DlogO(1)n) distributed optimization algorithms on such topologies, e.g., for MST and Min-Cut approximation, given that one can efficiently construct these shortcuts in a distributed manner.

We present a faster distributed broadcasting primitive for the classical radio network model.

We study efficient, deterministic interactive coding schemes that simulate any interactive protocol both under random and adversarial errors, and can achieve a constant communication rate independent of the protocol length. For channels that flip bits independently with probability ∊ < 1/2, our coding scheme achieves a communication rate of and a failure probability of exp(−n/log n) in length n protocols. Prior to our work, all nontrivial deterministic schemes (either efficient or not) had a rate bounded away from 1. Furthermore, the best failure probability achievable by an efficient deterministic coding scheme with constant rate was only quasi-polynomial, i.e., of the form exp(− logO(1) n) (Braverman, ITCS 2012). For channels in which an adversary controls the noise pattern our coding scheme can tolerate Ω(1/log n) fraction of errors with rate approaching 1. Once more, all previously known nontrivial deterministic schemes (either efficient or not) in the adversarial setting had a rate bounded away from 1, and no nontrivial efficient deterministic coding schemes were known with any constant rate. Essential to both results is an explicit, efficiently encodable and decodable systematic tree code of length n that has relative distance Ω(1/log n) and rate approaching 1, defined over an O(log n)-bit alphabet. No nontrivial tree code (either efficient or not) was known to approach rate 1, and no nontrivial distance bound was known for any efficient constant rate tree code. The fact that our tree code is systematic, turns out to play an important role in obtaining rate in the random error model, and approaching rate 1 in the adversarial error model.

This paper introduces the concept of low-congestion shortcuts for (near-)planar networks, and demonstrates their power by using them to obtain near-optimal distributed algorithms for problems such as Minimum Spanning Tree (MST) or Minimum Cut, in planar networks. Consider a graph G = (V, E) and a partitioning of V into subsets of nodes S1, …, SN, each inducing a connected subgraph G[Si]. We define an α-congestion shortcut with dilation β to be a set of subgraphs H1, …, HN ⊆ G, one for each subset Si, such that 1. For each i ∊ [1, N], the diameter of the subgraph G[Si] + Hi is at most β. 2. For each edge e ∊ E, the number of subgraphs G[Si] + Hi containing e is at most α. We prove that any partition of a D-diameter planar graph into individually-connected parts admits an O(D log D)-congestion shortcut with dilation O(D log D), and we also present a distributed construction of it in Õ(D) rounds. We moreover prove these parameters to be near-optimal; i.e., there are instances in which, unavoidably, . Finally, we use low-congestion shortcuts, and their efficient distributed construction, to derive Õ(D)-round distributed algorithms for MST and Min-Cut, in planar networks. This complexity nearly matches the trivial lower bound of Ω(D). We remark that this is the first result bypassing the well-known existential lower bound of general graphs (see Peleg and Rubinovich [FOCS'99]; Elkin [STOC'04]; and Das Sarma et al. [STOC'11]) in a family of graphs of interest.

We study coding schemes for multiparty interactive communication over synchronous networks that suffer from stochastic noise, where each bit is independently flipped with probability ε. We analyze the minimal overhead that must be added by the coding scheme in order to succeed in performing the computation despite the noise. Our main result is a lower bound on the communication of any noise-resilient protocol over a synchronous star network with n-parties (where all parties communicate in every round). Specifically, we show a task that can be solved by communicating T bits over the noise-free network, but for which any protocol with success probability of 1-o(1) must communicate at least Ω(T log n / log log n) bits when the channels are noisy. By a 1994 result of Rajagopalan and Schulman, the slowdown we prove is the highest one can obtain on any topology, up to a log log n factor. We complete our lower bound with a matching coding scheme that achieves the same overhead; thus, the capacity of (synchronous) star networks is Θ(log log n / log n). Our bounds prove that, despite several previous coding schemes with rate Ω(1) for certain topologies, no coding scheme with constant rate Ω(1) exists for arbitrary n-party noisy networks.

We introduce projection analysis—a new technique to analyze the stopping time of protocols that are based on random linear network coding (RLNC). Projection analysis drastically simplifies, extends, and strengthens previous results on RLNC gossip protocols. We analyze RLNC gossip in a general framework for network and communication models that encompasses and unifies the models used previously in this context. We show, in most settings for the first time, that the RLNC gossip converges with high probability in optimal time. Most stopping times are of the form O ( k + T ), where k is the number of messages to be distributed and T is the time it takes to disseminate one message. This means RLNC gossip achieves “perfect pipelining.” Our analysis directly extends to highly dynamic networks in which the topology can change completely at any time. This remains true, even if the network dynamics are controlled by a fully adaptive adversary that knows the complete network state. Virtually nothing besides simple O ( kT ) sequential flooding protocols was previously known for such a setting. While RLNC gossip works in this wide variety of networks our analysis remains the same and extremely simple. This contrasts with more complex proofs that were put forward to give less strong results for various special cases.

We provide tight upper and lower bounds on the noise resilience of interactive communication over noisy channels with feedback. In this setting, we show that the maximal fraction of noise that any nonadaptive protocol can withstand is 1/3. In addition, we provide a simple and efficient nonadaptive coding scheme that succeeds as long as the fraction of noise is at most 1/3 - ε. Surprisingly, both bounds hold regardless of whether the parties send bits or symbols from an arbitrarily large alphabet. We also consider interactive communication over erasure channels. We provide a coding scheme that withstands the optimal tolerable erasure rate of 1/2 - ε [Franklin et al., IEEE Trans. Info. Theory, 2015], but operates in a much simpler and more efficient way than the previous schemes. Our coding scheme works with an alphabet of size 4, in contrast to prior schemes in which the alphabet size grows as ε → 0. Building on the above algorithm with a fixed alphabet size, we are able to devise a protocol for binary erasure channels that tolerates erasure rates of up to 1/3 - ε.

This paper initiates the study of locally self-adjusting networks: networks whose topology adapts dynamically and in a decentralized manner, to the communication pattern σ. Our vision can be seen as a distributed generalization of the self-adjusting datastructures introduced by Sleator and Tarjan, 1985: In contrast to their splay trees which dynamically optimize the lookup costs from a single node (namely the tree root), we seek to minimize the routing cost between arbitrary communication pairs in the network. As a first step, we study distributed binary search trees (BSTs), which are attractive for their support of greedy routing. We introduce a simple model which captures the fundamental tradeoff between the benefits and costs of self-adjusting networks. We present the SplayNet algorithm and formally analyze its performance, and prove its optimality in specific case studies. We also introduce lower bound techniques based on interval cuts and edge expansion, to study the limitations of any demand-optimized network. Finally, we extend our study to multi-tree networks, and highlight an intriguing difference between classic and distributed splay trees.

We introduce the notion of one-way communication schemes with partial noiseless feedback. In this setting, Alice wishes to communicate a message to Bob by using a communication scheme that involves sending a sequence of bits over a channel while receiving feedback bits from Bob for delta fraction of the transmissions. An adversary is allowed to corrupt up to a constant fraction of Alice's transmissions, while the feedback is always uncorrupted. Motivated by questions related to coding for interactive communication, we seek to determine the maximum error rate, as a function of 0 <= delta <= 1, such that Alice can send a message to Bob via some protocol with delta fraction of noiseless feedback. The case delta = 1 corresponds to full feedback, in which the result of Berlekamp ['64] implies that the maximum tolerable error rate is 1/3, while the case delta = 0 corresponds to no feedback, in which the maximum tolerable error rate is 1/4, achievable by use of a binary error-correcting code. In this work, we show that for any delta in (0,1] and gamma in [0, 1/3), there exists a randomized communication scheme with noiseless delta-feedback, such that the probability of miscommunication is low, as long as no more than a gamma fraction of the rounds are corrupted. Moreover, we show that for any delta in (0, 1] and gamma < f(delta), there exists a deterministic communication scheme with noiseless delta-feedback that always decodes correctly as long as no more than a gamma fraction of rounds are corrupted. Here f is a monotonically increasing, piecewise linear, continuous function with f(0) = 1/4 and f(1) = 1/3. Also, the rate of communication in both cases is constant (dependent on delta and gamma but independent of the input length).

We provide tight upper and lower bounds on the noise resilience of interactive communication over noisy channels with feedback. In this setting, we show that the maximal fraction of noise that any robust protocol can resist is 1/3. Additionally, we provide a simple and efficient robust protocol that succeeds as long as the fraction of noise is at most 1/3--ε. Surprisingly, both bounds hold regardless of whether the parties send bits or symbols from an arbitrarily large alphabet.

We present a new distributed algorithm for the resource discovery problem introduced by Harchol-Balter, Leighton, and Levin in PODC'99. The resource discovery problem consists of a synchronous network with n machines in which at any timestep any machine v can PUSH or PULL a message to/from any other machine u whose (IP) address is known to v. Messages can contain addresses which then change the "topology". The goal of a distributed resource discovery problem is to enable all machines to learn the addresses of all other machines as fast as possible while keeping the number of messages sent low.

A fundamental result by Karger [10] states that for any λ-edge-connected graph with n nodes, independently sampling each edge with probability p = Ω(log n/λ) results in a graph that has edge connectivity Ω(λp), with high probability. This paper proves the analogous result for vertex connectivity, when sampling vertices. We show that for any k-vertex-connected graph G with n nodes, if each node is independently sampled with probability , then the subgraph induced by the sampled nodes has vertex connectivity Ω(kp2), with high probability. This bound improves upon the recent results of Censor-Hillel et al. [6], and is existentially optimal.

We consider interactive communication performed over two simple types of noisy channels: binary error channels with noiseless feedback and binary erasure channels. In both cases, the noise model is adversarial Assuming at most ε-fraction of the bits can be corrupted, we show coding schemes that simulate any alternating interactive protocol with rate 1 — Θ(H(ε)). All our simulations are simple, randomized, and computationally efficient. The rates of our coding schemes stand in contrast to the interactive communication rates supported by random or adversarial error channels without feedback, for which the best known coding schemes achieve rates of and , respectively. As these rates are conjectured to be optimal, our result implies a large asymptotic gap between interactive communication rate over noisy channels with and without feedback. Such a gap has no equivalent in the standard one-way communication setting.

We study gossip algorithms for the rumor spreading problem, which asks each node to deliver a rumor to all nodes in an unknown network. Gossip algorithms allow nodes only to call one neighbor per round and have recently attracted attention as message efficient, simple, and robust solutions to the rumor spreading problem. A long series of papers analyzed the performance of uniform random gossip in which nodes repeatedly call a random neighbor to exchange all rumors with. A main result of this investigation was that uniform gossip completes in O (log n /Φ) rounds where Φ is the conductance of the network. Nonuniform random gossip schemes were devised to allow efficient rumor spreading in networks with bottlenecks. In particular, [Censor-Hillel et al., STOC&12] gave an O (log 3 n ) algorithm to solve the 1-local broadcast problem in which each node wants to exchange rumors locally with its 1-neighborhood. By repeatedly applying this protocol, one can solve the global rumor spreading quickly for all networks with small diameter, independently of the conductance. All these algorithms are inherently randomized in their design and analysis. A parallel research direction has been to reduce and determine the amount of randomness needed for efficient rumor spreading. This has been done via lower bounds for restricted models and by designing gossip algorithms with a reduced need for randomness, for instance, by using pseudorandom generators with short random seeds. The general intuition and consensus of these results has been that randomization plays a important role in effectively spreading rumors and that at least a polylogarithmic number of random bit are crucially needed. In this article improves over the state of the art in several ways by presenting a deterministic gossip algorithm that solves the the k -local broadcast problem in 2( k + log 2 n ) log 2 n rounds. Besides being the first efficient deterministic solution to the rumor spreading problem this algorithm is interesting in many aspects: It is simpler, more natural, more robust, and faster than its randomized pendant and guarantees success with certainty instead of with high probability. Its analysis is furthermore simple, self-contained, and fundamentally different from prior works.

In this paper, we design and analyze information spreading algorithms for dynamic networks with correlated data. In these networks, either the data to be distributed, the data already available at the nodes, or both are correlated. Moreover, nodes' availability and connectivity is dynamic - a scenario typical for wireless networks. Our contribution is twofold. First, although coding schemes for correlated data have been studied extensively, the focus has been on characterizing the rate region in static networks. In an information spreading scheme, however, nodes may communicate by continuously exchanging packets according to some underlying communication model. The main figure of merit is the stopping time - the time required until nodes can successfully decode. While information spreading schemes, such as gossip, are practical, distributed, and scalable, they have only been studied for uncorrelated data. We close this gap by providing techniques to analyze network-coded information spreading in dynamic networks with correlated data. Second, we give a clean framework for oblivious dynamic network models that in particular applies to a multitude of wireless network and communication scenarios. We specify a general setting for the data model and give tight bounds on the stopping times of network-coded protocols in this wide range of settings. En route, we analyze the capacities seen by nodes under a network-coded information spreading protocol, a previously unexplored question. We conclude with extensive simulations, clearly validating the key trends and phenomena predicted in the analysis.

Since the invention of AVL trees in 1962, many kinds of binary search trees have been proposed. Notable are red-black trees, in which bottom-up rebalancing after an insertion or deletion takes O(1) amortized time and O(1) rotations worst-case. But the design space of balanced trees has not been fully explored. We continue the exploration. Our contributions are three: We systematically study the use of ranks and rank differences to define height-based balance in binary trees. Different invariants on rank differences yield AVL trees, red-black trees, and other kinds of balanced trees. By relaxing AVL trees, we obtain a new kind of balanced binary tree, the weak AVL tree (wavl tree) , whose properties we develop. Bottom-up rebalancing after an insertion or deletion takes O(1) amortized time and at most two rotations, improving the three or more rotations per deletion needed in all other kinds of balanced trees of which we are aware. The height bound of a wavl tree degrades gracefully from that of an AVL tree as the number of deletions increases and is never worse than that of a red-black tree. Wavl trees also support top-down, fixed look-ahead rebalancing in O(1) amortized time. Finally, we use exponential potential functions to prove that in wavl trees rebalancing steps occur exponentially infrequently in rank. Thus, most of the rebalancing is at the bottom of the tree, which is crucial in concurrent applications and in those in which rotations take time that depends on the subtree size.

We study coding schemes for error correction in interactive communications. Such interactive coding schemes simulate any n-round interactive protocol using N rounds over an adversarial channel that corrupts up to ρN transmissions. Important performance measures for a coding scheme are its maximum tolerable error rate ρ, communication complexity N, and computational complexity. We give the first coding scheme for the standard setting which performs optimally in all three measures: Our randomized non-adaptive coding scheme has a near-linear computational complexity and tolerates any error rate δ1.

We provide the first capacity approaching coding schemes that robustly simulate any interactive protocol over an adversarial channel that corrupts any fraction of the transmitted symbols. Our coding schemes achieve a communication rate of 1 - O(∈√loglog1/∈) can be improved to 1 - O(√∈) for random, oblivious, and over any adversarial channel. This computationally bounded channels, or if parties have shared randomness unknown to the channel. Surprisingly, these rates exceed the 1 - Ω( H(ϵ)) = 1 - Ω(ϵ√log1/ϵ) interactive channel capacity bound which [Kol and Raz; STOC'13] recently proved for random errors. We conjecture 1- Θ(ϵ log log 1/ϵ) and 1- Θ(√ϵ) to be the optimal rates for their respective settings and therefore to capture the interactive channel capacity for random and adversarial errors. In addition to being very communication efficient, our randomized coding schemes have multiple other advantages. They are computationally efficient, extremely natural, and significantly simpler than prior (non-capacity approaching) schemes. In particular, our protocols do not employ any coding but allow the original protocol to be performed as-is, interspersed only by short exchanges of hash values. When hash values do not match, the parties backtrack. Our approach is, as we feel, by far the simplest and most natural explanation for why and how robust interactive communication in a noisy environment is possible.

We consider the question of the capacity of the deletion channel when multiple copies of the codeword are sent independently through the channel. This type of coding problem is straightforward for standard erasure and error channels, but is more challenging in the deletion channel setting. Our results show that when the same codeword is sent k times through the channel, each time with independent deletions with probability d, the optimal communication rate under a random codebook is 1 - αH(dk) + O(dk) for an explicitly given constant α.

Distributed computing models typically assume reliable communication between processors. While such assumptions often hold for engineered networks, e.g., due to underlying error correction protocols, their relevance to biological systems, wherein messages are often distorted before reaching their destination, is quite limited. In this study we aim at bridging this gap by rigorously analyzing a model of communication in large anonymous populations composed of simple agents which interact through short and highly unreliable messages. We focus on the rumor-spreading problem and the majority-consensus problem, two fundamental tasks in distributed computing, and initiate their study under communication noise. Our model for communication is extremely weak and follows the push gossip communication paradigm: In each synchronous round each agent that wishes to send information delivers a message to a random anonymous agent. This communication is further restricted to contain only one bit (essentially representing an opinion). Lastly, the system is assumed to be so noisy that the bit in each message sent is flipped independently with probability 1/2-ε, for some small Aε >0.

Gossip algorithms spread information in distributed networks by nodes repeatedly forwarding information to a few random contacts. By their very nature, gossip algorithms tend to be distributed and fault tolerant. If done right, they can also be fast and message-efficient. A common model for gossip communication is the random phone call model, in which in each synchronous round each node can PUSH or PULL information to or from a random other node. For example, Karp et al. [FOCS 2000] gave algorithms in this model that spread a message to all nodes in Θ(log n) rounds while sending only O(log log n) messages per node on average. They also showed that at least Θ(log n) rounds are necessary in this model and that algorithms achieving this round-complexity need to send ω(1) messages per node on average. Recently, Avin and Elsasser [DISC 2013], studied the random phone call model with the natural and commonly used assumption of direct addressing. Direct addressing allows nodes to directly contact nodes whose ID (e.g., IP address) was learned before. They show that in this setting, one can "break the log n barrier" and achieve a gossip algorithm running in O(√log n) rounds, albeit while using O(√log n) messages per node.

The broadcast throughput in a network is defined as the average number of messages that can be transmitted per unit time from a given source to all other nodes when time goes to infinity. Classical broadcast algorithms treat messages as atomic tokens and route them from the source to the receivers by making intermediate nodes store and forward messages. The more recent network coding approach, in contrast, prompts intermediate nodes to mix and code together messages. It has been shown that certain wired networks have an asymptotic network coding gap, that is, they have asymptotically higher broadcast throughput when using network coding compared to routing. Whether such a gap exists for wireless networks has been an open question of great interest. We approach this question by studying the broadcast throughput of the radio network model which has been a standard mathematical model to study wireless communication. We show that there is a family of radio networks with a tight Θ(log log n) network coding gap, that is, networks in which the asymptotic throughput achievable via routing messages is a Θ(log log n) factor smaller than that of the optimal network coding algorithm. We also provide new tight upper and lower bounds showing that the asymptotic worst-case broadcast throughput over all networks with n nodes is messages-per-round for both routing and network coding.

We consider the task of interactive communication in the presence of adversarial errors and present tight bounds on the tolerable error-rates in a number of different settings.

This paper initiates the study of self-adjusting networks (or distributed data structures) whose topologies dynamically adapt to a communication pattern σ. We present a fully decentralized self-adjusting solution called SplayNet. A SplayNet is a distributed generalization of the classic splay tree concept. It ensures short paths (which can be found using local-greedy routing) between communication partners while minimizing topological rearrangements. We derive an upper bound for the amortized communication cost of a SplayNet based on empirical entropies of σ, and show that SplayNets have several interesting convergence properties. For instance, SplayNets features a provable online optimality under special requests scenarios. We also investigate the optimal static network and prove different lower bounds for the average communication cost based on graph cuts and on the empirical entropy of the communication pattern σ. From these lower bounds it follows, e.g., that SplayNets are optimal in scenarios where the requests follow a product distribution as well. Finally, this paper shows that in contrast to the Minimum Linear Arrangement problem which is generally NP-hard, the optimal static tree network can be computed in polynomial time for any guest graph, despite the exponentially large graph family. We complement our formal analysis with a small simulation study on a Facebook graph.

We present a randomized distributed algorithm that in radio networks with collision detection broadcasts a single message in O(D + log6 n) rounds, with high probability. This time complexity is most interesting because of its optimal additive dependence on the network diameter D. It improves over the currently best known O(Dlogn/D + log2 n) algorithms, due to Czumaj and Rytter [FOCS 2003], and Kowalski and Pelc [PODC 2003]. These algorithms where designed for the model without collision detection and are optimal in that model. However, as explicitly stated by Peleg in his 2007 survey on broadcast in radio networks, it had remained an open question whether the bound can be improved with collision detection.

We design leader election protocols for multi-hop radio networks that elect a leader in almost the same time TBC that it takes for broadcasting one message (one ID). For the setting without collision detection our algorithm runs whp. in rounds on any n-node network with diameter D. Since is a lower bound, our upper bound is optimal up to a factor of at most log log n and the extra log n factor on the additive term. Our algorithm is furthermore the first O(n) time algorithm for this setting. Our algorithm improves over a 23 year old simulation approach of Bar-Yehuda, Goldreich and Itai with a O(TBC log n) running time: In 1987 they designed a fast broadcast protocol and subsequently in 1989 they showed how it can be used to simulate one round of a single-hop network that has collision detection in TBC time. The prime application of this simulation was to simulate Willards single-hop leader election protocol, which elects a leader in O(log n) rounds whp. and O(log log n) rounds in expectation. While it was subsequently shown that Willards bounds are tight, it was unclear whether the simulation approach is optimal. Our results break this barrier and essentially remove the logarithmic slowdown over the broadcast time TBC. This is achieved by going away from the simulation approach. We also give an leader election algorithm for the setting with collision detection (even with single-bit messages). This is optimal up to log log n factors and improves over a deterministic algorithm that requires Θ(n) rounds independently of D. Our almost optimal leader election protocols are especially important because countless communication protocols in radio networks use leader election as a crucial first step to solve various, seemingly unrelated, communication primitives such as gathering, multiple unicasts or multiple broadcasts. Even though leader election seems easier than these tasks, its best-known O(TBC log n) running time had become a bottleneck, preventing optimal algorithms. Breaking the simulation barrier for leader election in this paper has subsequently led to the development of near optimal protocols for these communication primitives.

We study gossip algorithms for the rumor spreading problem which asks each node to deliver a rumor to all nodes in an unknown network. Gossip algorithms allow nodes only to call one neighbor per round and have recently attracted attention as message efficient, simple and robust solutions to the rumor spreading problem.

The Lovász local lemma (LLL) [P. Erdös and L. Lovász, Problems and results on 3-chromatic hypergraphs and some related questions, in Infinite and Finite Sets, Vol. II, A. Hajnal, R. Rado, and V. T. Sós, eds., North--Holland, Amsterdam, 1975, pp. 609--627] is a powerful result in probability theory that informally states the following: the probability that none of a set of bad events happens is positive if the probability of each event is small compared to the number of events that depend on it. The LLL is often used for nonconstructive existence proofs of combinatorial structures. A prominent application is to $k$-CNF formulas, where the LLL implies that if every clause in a formula shares variables with at most $d \leq 2^k/e-1$ other clauses, then such a formula has a satisfying assignment. Recently, a randomized algorithm to efficiently construct a satisfying assignment in this setting was given by Moser [A constructive proof of the Lovász local lemma, in STOC '09: Proceedings of the 41st Annual ACM Symposium on Theory of Computing, ACM, New York, 2009, pp. 343--350]. Subsequently Moser and Tardos [J. ACM, 57 (2010), pp. 11:1--11:15] gave a general algorithmic framework for the LLL and a randomized algorithm within this framework to construct the structures guaranteed by the LLL. The main problem left open by Moser and Tardos was to design an efficient deterministic algorithm for constructing structures guaranteed by the LLL. In this paper we provide such an algorithm. Our algorithm works in the general framework of Moser and Tardos with a minimal loss in parameters. For the special case of constructing satisfying assignments for $k$-CNF formulas with $m$ clauses, where each clause shares variables with at most $d \leq 2^{k/(1+\epsilon)}/e - 1$ other clauses, for any $\epsilon\in (0,1)$, we give a deterministic algorithm that finds a satisfying assignment in time $\tilde{O}(m^{2(1+1/\epsilon)})$. This improves upon the deterministic algorithms of Moser and of Moser and Tardos with running times $m^{\Omega(k^2)}$ and $m^{\Omega(d \log d)}$, respectively, which are superpolynomial for $k=\omega(1)$ and $d=\omega(1)$, and upon the previous best deterministic algorithm of Beck, which runs in polynomial time only for $d\leq 2^{k/16}/4$. Our algorithm is the first deterministic algorithm that works in the general framework of Moser and Tardos. We also give a parallel NC algorithm for the same setting, improving upon an algorithm of Alon [Random Structures Algorithms, 2 (1991), pp. 367--378].

We design and analyze gossip algorithms for networks with correlated data. In these networks, either the data to be distributed, the data already available at the nodes, or both, are correlated. Although coding schemes for correlated data have been studied extensively, the focus has been on characterizing the rate region in static memory-free networks. In a gossip-based scheme, however, nodes communicate among each other by continuously exchanging packets according to some underlying communication model. The main figure of merit in this setting is the stopping time - the time required until nodes can successfully decode. While Gossip schemes are practical, distributed and scalable, they have only been studied for uncorrelated data. We wish to close this gap by providing techniques to analyze network coded gossip in (dynamic) networks with correlated data. We give a clean framework for oblivious network models that applies to a multitude of network and communication scenarios, specify a general setting for distributed correlated data, and give tight bounds on the stopping times of network coded protocols in this wide range of scenarios.

We study randomized gossip-based processes in dynamic networks that are motivated by information discovery in large-scale distributed networks such as peer-to-peer and social networks. A well-studied problem in peer-to-peer networks is resource discovery, where the goal for nodes (hosts with IP addresses) is to discover the IP addresses of all other hosts. Also, some of the recent work on self-stabilization algorithms for P2P/overlay networks proceed via discovery of the complete network. In social networks, nodes (people) discover new nodes through exchanging contacts with their neighbors (friends). In both cases the discovery of new nodes changes the underlying network --- new edges are added to the network --- and the process continues in the changed network. Rigorously analyzing such dynamic (stochastic) processes in a continuously changing topology remains a challenging problem with obvious applications.

In this paper, we study the question of how efficiently a collection of interconnected nodes can perform a global computation in the GOSSIP model of communication. In this model, nodes do not know the global topology of the network, and they may only initiate contact with a single neighbor in each round. This model contrasts with the much less restrictive LOCAL model, where a node may simultaneously communicate with all of its neighbors in a single round. A basic question in this setting is how many rounds of communication are required for the information dissemination problem, in which each node has some piece of information and is required to collect all others. In the LOCAL model, this is quite simple: each node broadcasts all of its information in each round, and the number of rounds required will be equal to the diameter of the underlying communication graph. In the GOSSIP model, each node must independently choose a single neighbor to contact, and the lack of global information makes it difficult to make any sort of principled choice. As such, researchers have focused on the uniform gossip algorithm, in which each node independently selects a neighbor uniformly at random. When the graph is well-connected, this works quite well. In a string of beautiful papers, researchers proved a sequence of successively stronger bounds on the number of rounds required in terms of the conductance φ and graph size n, culminating in a bound of O(φ-1 log n).

We present two online algorithms for maintaining a topological order of a directed n -vertex acyclic graph as arcs are added, and detecting a cycle when one is created. Our first algorithm handles m arc additions in O( m 3/2 ) time. For sparse graphs ( m / n = O(1)), this bound improves the best previous bound by a logarithmic factor, and is tight to within a constant factor among algorithms satisfying a natural locality property. Our second algorithm handles an arbitrary sequence of arc additions in O( n 5/2 ) time. For sufficiently dense graphs, this bound improves the best previous bound by a polynomial factor. Our bound may be far from tight: we show that the algorithm can take Ω( n 2 2 √2 lg n ) time by relating its performance to a generalization of the k -levels problem of combinatorial geometry. A completely different algorithm running in Θ( n 2 log n ) time was given recently by Bender, Fineman, and Gilbert. We extend both of our algorithms to the maintenance of strong components, without affecting the asymptotic time bounds.

Random Linear Network Coding (RLNC) has emerged as a powerful tool for robust high-throughput multicast. Projection analysis, a recently introduced technique, shows that the distributed packetized RLNC protocol achieves (order) optimal and perfectly pipelined information dissemination in many settings. In the original approach to RNLC intermediate nodes code together all available information. This requires intermediate nodes to keep considerable data available for coding. Moreover, it results in a coding complexity that grows linearly with the size of this data. While this has been identified as a problem, approaches that combine queuing theory and network coding have heretofore not provided a succinct representation of the memory needs of network coding at intermediates nodes. This paper shows the surprising result that, in all settings with a continuous stream of data, network coding continues to perform optimally even if only one packet per node is kept in active memory and used for computations. This leads to an extremely simple RLNC protocol variant with drastically reduced requirements on computational and memory resources. By extending the projection analysis, we show that in all settings in which the RLNC protocol was proven to be optimal its finite memory variant performs equally well. In the same way as the original projection analysis, our technique applies in a wide variety of network models, including highly dynamic topologies that can change completely at any time in an adversarial fashion.

We analyze distributed and packetized implementations of random linear network coding (PNC) with buffers. In these protocols, nodes store received packets to later produce coded packets that reflect this information. We show the optimality of PNC for any buffer size; i.e., we show that PNC performs at least as good as any protocols with the same buffer size. In other words, a multicast task completes at exactly the first time in which in hindsight it was possible to route information from the sources to each receiver individually given the buffer constraint, i.e., that the buffer used at each node never exceeds its buffer size. This shows that PNC, even without any feedback or explicit buffer management, allows to keep minimal buffer sizes while maintaining its optimal performance.

We use network coding to improve the speed of distributed computation in the dynamic network model of Kuhn, Lynch and Oshman [STOC '10]. In this model an adversary adaptively chooses a new network topology in every round, making even basic distributed computations challenging. Kuhn et al. show that n nodes, each starting with a d-bit token, can broadcast them to all nodes in time O(n[superscript 2]) using b-bit messages, where b > d + log n. Their algorithms take the natural approach of token forwarding: in every round each node broadcasts some particular token it knows. They prove matching Ω(n[superscript 2]) lower bounds for a natural class of token forwarding algorithms and an Ω(n log n) lower bound that applies to all token-forwarding algorithms. We use network coding, transmitting random linear combinations of tokens, to break both lower bounds. Our algorithm's performance is quadratic in the message size b, broadcasting the n tokens in roughly d/b[superscript 2] * n[superscript 2] rounds. For b = d = Θ(log n) our algorithms use O(n[superscript 2]/log n) rounds, breaking the first lower bound, while for larger message sizes we obtain linear-time algorithms. We also consider networks that change only every T rounds, and achieve an additional factor T[superscript 2] speedup. This contrasts with related lower and upper bounds of Kuhn et al. implying that for natural token-forwarding algorithms a speedup of T, but not more, can be obtained. Lastly, we give a general way to derandomize random linear network coding, that also leads to new deterministic information dissemination algorithms.

We introduce projection analysis - a new technique to analyze the stopping time of gossip protocols that are based on random linear network coding (RLNC). Projection analysis drastically simplifies, extends and strengthens previous results. We analyze RLNC gossip in a general framework for network and communication models that encompasses and unifies the models used previously in this context. We show, in most settings for the first time, that the RLNC gossip converges with high probability in optimal time. Most stopping times are of the form O(k + T), where k is the number of messages to be distributed and T is the time it takes to disseminate one message. This means RLNC gossip achieves perfect pipelining. Our analysis directly extends to highly dynamic networks in which the topology can change completely at any time. This remains true, even if the network dynamics are controlled by a fully adaptive adversary that knows the complete network state. Virtually nothing besides simple O(kT) sequential flooding protocols was previously known for such a setting.While RLNC gossip works in this wide variety of networks our analysis remains the same and extremely simple. This contrasts with more complex proofs that were put forward to give less strong results for various special cases.

The Lovász Local Lemma (LLL) is a powerful tool that gives sufficient conditions for avoiding all of a given set of “bad” events, with positive probability. A series of results have provided algorithms to efficiently construct structures whose existence is non-constructively guaranteed by the LLL, culminating in the recent breakthrough of Moser and Tardos [2010] for the full asymmetric LLL. We show that the output distribution of the Moser-Tardos algorithm well-approximates the conditional LLL-distribution , the distribution obtained by conditioning on all bad events being avoided. We show how a known bound on the probabilities of events in this distribution can be used for further probabilistic analysis and give new constructive and nonconstructive results. We also show that when a LLL application provides a small amount of slack, the number of resamplings of the Moser-Tardos algorithm is nearly linear in the number of underlying independent variables (not events!), and can thus be used to give efficient constructions in cases where the underlying proof applies the LLL to super-polynomially many events. Even in cases where finding a bad event that holds is computationally hard, we show that applying the algorithm to avoid a polynomial-sized “core” subset of bad events leads to a desired outcome with high probability. This is shown via a simple union bound over the probabilities of non-core events in the conditional LLL-distribution, and automatically leads to simple and efficient Monte-Carlo (and in most cases RNC ) algorithms. We demonstrate this idea on several applications. We give the first constant-factor approximation algorithm for the Santa Claus problem by making a LLL-based proof of Feige constructive. We provide Monte Carlo algorithms for acyclic edge coloring, nonrepetitive graph colorings, and Ramsey-type graphs. In all these applications, the algorithm falls directly out of the non-constructive LLL-based proof. Our algorithms are very simple, often provide better bounds than previous algorithms, and are in several cases the first efficient algorithms known. As a second type of application we show that the properties of the conditional LLL-distribution can be used in cases beyond the critical dependency threshold of the LLL: avoiding all bad events is impossible in these cases. As the first (even nonconstructive) result of this kind, we show that by sampling a selected smaller core from the LLL-distribution, we can avoid a fraction of bad events that is higher than the expectation. MAX k -SAT is an illustrative example of this.

We introduce the rank-pairing heap, an implementation of heaps that combines the asymptotic efficiency of Fibonacci heaps with much of the simplicity of pairing heaps. Other heap implementations that match the bounds of Fibonacci heaps do so by maintaining a balance condition on the trees representing the heap. In contrast to these structures but like pairing heaps, our trees can evolve to have arbitrary (unbalanced) structure. Also like pairing heaps, our structure requires at most one cut and no other restructuring per key decrease, in the worst case: the only changes that can cascade during a key decrease are changes in node ranks. Although our data structure is simple, its analysis is not.

The Lovász Local Lemma (LLL) is a powerful tool that gives sufficient conditions for avoiding all of a given set of "bad" events, with positive probability. A series of results have provided algorithms to efficiently construct structures whose existence is non-constructively guaranteed by the LLL, culminating in the recent breakthrough of Moser & Tardos. We show that the output distribution of the Moser-Tardos algorithm well-approximates the conditional LLL-distribution - the distribution obtained by conditioning on all bad events being avoided. We show how a known bound on the probabilities of events in this distribution can be used for further probabilistic analysis and give new constructive and non-constructive results. We also show that when an LLL application provides a small amount of slack, the number of resamplings of the Moser-Tardos algorithm is nearly linear in the number of underlying independent variables (not events!), and can thus be used to give efficient constructions in cases where the underlying proof applies the LLL to super-polynomially many events. Even in cases where finding a bad event that holds is computationally hard, we show that applying the algorithm to avoid a polynomial-sized "core" subset of bad events leads to a desired outcome with high probability. We demonstrate this idea on several applications. We give the first constant-factor approximation algorithm for the Santa Claus problem by making an LLL-based proof of Feige constructive. We provide Monte Carlo algorithms for acyclic edge coloring, non-repetitive graph colorings, and Ramsey-type graphs. In all these applications the algorithm falls directly out of the non-constructive LLL-based proof. Our algorithms are very simple, often provide better bounds than previous algorithms, and are in several cases the first efficient algorithms known. As a second type of application we consider settings beyond the critical dependency threshold of the LLL: avoiding all bad events is impossible in these cases. As the first (even non-constructive) result of this kind, we show that by sampling from the LLL-distribution of a selected smaller core, we can avoid a fraction of bad events that is higher than the expectation. MAX k-SAT is an example of this.

The Lovász Local Lemma [5] (LLL) is a powerful result in probability theory that states that the probability that none of a set of bad events happens is nonzero if the probability of each event is small compared to the number of events that depend on it. It is often used in combination with the probabilistic method for non-constructive existence proofs. A prominent application is to k-CNF formulas, where LLL implies that, if every clause in the formula shares variables with at most d ≤ 2k/e other clauses then such a formula has a satisfying assignment. Recently, a randomized algorithm to efficiently construct a satisfying assignment was given by Moser [13]. Subsequently Moser and Tardos [14] gave a randomized algorithm to construct the structures guaranteed by the LLL in a very general algorithmic framework. We address the main problem left open by Moser and Tardos of derandomizing these algorithms efficiently. Specifically, for a k-CNF formula with m clauses and d ≤ 2k/(1+ε)/e for some ε ∊ (0, 1), we give an algorithm that finds a satisfying assignment in time . This improves upon the deterministic algorithms of Moser and of Moser-Tardos with running times mΩ(k2) and mΩ(k · 1.ε) which are superpolynomial for k = ω(1) and upon other previous algorithms which work only for d ≤ 2k/16/e. Our algorithm works efficiently for the asymmetric version of LLL under the algorithmic framework of Moser and Tardos [14] and is also parallelizable, i.e., has polylogarithmic running time using polynomially many processors.