Given an n-point metric space (X,d_X), a tree cover 𝒯 is a set of |𝒯| = k trees on X such that every pair of vertices in X has a low-distortion path in one of the trees in 𝒯. Tree covers have been playing a crucial role in graph algorithms for decades, and the research focus is the construction of tree covers with small size k and distortion. When k = 1, the best distortion is known to be Θ(n). For a constant k ≥ 2, the best distortion upper bound is Õ(n^{1/k}) and the strongest lower bound is Ω(log_k n), leaving a gap to be closed. In this paper, we improve the lower bound to Ω(n^{1/(2^{k-1)}}). Our proof is a novel analysis on a structurally simple grid-like graph, which utilizes some combinatorial fixed-point theorems. We believe that they will prove useful for analyzing other tree-like data structures as well.

Given a large graph G with a set of its k vertices called terminals, a quality-q flow sparsifier is a small graph G′ that contains the terminals and preserves all multicommodity flows between them up to some multiplicative factor q≥ 1, called the quality. Constructing flow sparsifiers with good quality and small size (|V(G′)|) has been a central problem in graph compression.

We study vertex sparsification for preserving cuts. Given a graph G with a subset |T| = k of its vertices called terminals, a quality-q cut sparsifier is a graph G' that contains T, such that, for any partition (T₁,T₂) of T into non-empty subsets, the value of the min-cut in G' separating T₁ from T₂ is within factor q from the value of the min-cut in G separating T₁ from T₂. The construction of cut sparsifiers with good (small) quality and size has been a central problem in graph compression for years. Planar graphs and quasi-bipartite graphs are two important special families studied in this research direction. The main results in this paper are new cut sparsifier constructions for them in the high-quality regime (where q = 1 or 1+{ε} for small {ε} > 0). We first show that every planar graph admits a planar quality-(1+{ε}) cut sparsifier of size Õ(k/poly({ε})), which is in sharp contrast with the lower bound of 2^{Ω(k)} for the quality-1 case. We then show that every quasi-bipartite graph admits a quality-1 cut sparsifier of size 2^{Õ(k²)}. This is the second to improve over the doubly-exponential bound for general graphs (previously only planar graphs have been shown to have single-exponential size quality-1 cut sparsifiers). Lastly, we show that contraction, a common approach for constructing cut sparsifiers adopted in most previous works, does not always give optimal bounds for cut sparsifiers. We demonstrate this by showing that the optimal size bound for quality-(1+{ε}) contraction-based cut sparsifiers for quasi-bipartite graphs lies in the range [k^{̃Ω(1/{ε})},k^{O(1/{ε}²)}], while in previous work an upper bound of Õ(k/{ε}²) was achieved via a non-contraction approach.

We study the following distance realization problem. Given a quasi-metric D on a set T of terminals, does there exist a directed Okamura-Seymour graph that realizes D as the (directed) shortest-path distance metric on T? We show that, if we are further given the circular ordering of terminals lying on the boundary, then Monge property is a sufficient and necessary condition. This generalizes previous results for undirected Okamura-Seymour instances.

In this paper we study the problem of finding (ϵ, ϕ)-expander decompositions of a graph in the streaming model, in particular for dynamic streams of edge insertions and deletions. The goal is to partition the vertex set so that every component induces a ϕ-expander, while the number of inter-cluster edges is only an ϵ fraction of the total volume. It was recently shown that there exists a simple algorithm to construct a (O(ϕlog n), ϕ)-expander decomposition of an n-vertex graph using Oe(n/ϕ2) bits of space [Filtser, Kapralov, Makarov, ITCS’23]. This result calls for understanding the extent to which a dependence in space on the sparsity parameter ϕ is inherent. We move towards answering this question on two fronts. We prove that a (O(ϕlog n), ϕ)-expander decomposition can be found using Oe(n) space, for every ϕ. At the core of our result is the first streaming algorithm for computing boundary-linked expander decompositions, a recently introduced strengthening of the classical notion [Goranci et al., SODA’21]. The key advantage is that a classical sparsifier [Fung et al., STOC’11], with size independent of ϕ, preserves the cuts inside the clusters of a boundary-linked expander decomposition within a multiplicative error. Notable algorithmic applications use sequences of expander decompositions, in particular one often repeatedly computes a decomposition of the subgraph induced by the inter-cluster edges (e.g., the seminal work of Spielman and Teng on spectral sparsifiers [Spielman, Teng, SIAM Journal of Computing 40(4)], or the recent maximum flow breakthrough [Chen et al., FOCS’22], among others). We prove that any streaming algorithm that computes a sequence of (O(ϕlog n), ϕ)-expander decompositions requires Ω(e n/ϕ) bits of space, even in insertion only streams.

In the Steiner point removal (SPR) problem, we are given a (weighted) graph G and a subset T of its vertices called terminals, and the goal is to compute a (weighted) graph H on T that is a minor of G, such that the distance between every pair of terminals is preserved to within some small multiplicative factor, that is called the stretch of H.

Given a large graph G with a subset |T| = k of its vertices called terminals, a quality-q flow sparsifier is a small graph G’ that contains T and preserves all multicommodity flows that can be routed between terminals in T, to within factor q. The problem of constructing flow sparsifiers with good (small) quality and (small) size has been a central problem in graph compression for decades.

We consider the design of sublinear space and query complexity algorithms for estimating the cost of a minimum spanning tree (MST) and the cost of a minimum traveling salesman (TSP) tour in a metric on n points. We start by exploring this estimation task in the regime of o(n) space, when the input is presented as a stream of all binom(n,2) entries of the metric in an arbitrary order (a metric stream). For any α ≥ 2, we show that both MST and TSP cost can be α-approximated using Õ(n/α) space, and moreover, Ω(n/α²) space is necessary for this task. We further show that even if the streaming algorithm is allowed p passes over a metric stream, it still requires Ω̃(√{n/α p²}) space. We next consider the well-studied semi-streaming regime. In this regime, it is straightforward to compute MST cost exactly even in the case where the input stream only contains the edges of a weighted graph that induce the underlying metric (a graph stream), and the main challenging problem is to estimate TSP cost to within a factor that is strictly better than 2. We show that in graph streams, for any ε > 0, any one-pass (2-ε)-approximation of TSP cost requires Ω(ε² n²) space. On the other hand, we show that there is an Õ(n) space two-pass algorithm that approximates the TSP cost to within a factor of 1.96. Finally, we consider the query complexity of estimating metric TSP cost to within a factor that is strictly better than 2 when the algorithm is given access to an n × n matrix that specifies pairwise distances between n points. The problem of MST cost estimation in this model is well-understood and a (1+ε)-approximation is achievable by Õ(n/ε^{O(1)}) queries. However, for estimating TSP cost, it is known that an analogous result requires Ω(n²) queries even for (1,2)-TSP, and for general metrics, no algorithm that achieves a better than 2-approximation with o(n²) queries is known. We make progress on this task by designing an algorithm that performs Õ(n^{1.5}) distance queries and achieves a strictly better than 2-approximation when either the metric is known to contain a spanning tree supported on weight-1 edges or the algorithm is given access to a minimum spanning tree of the graph. Prior to our work, such results were only known for the special cases of graphic TSP and (1,2)-TSP. In terms of techniques, our algorithms for metric TSP cost estimation in both streaming and query settings rely on estimating the cover advantage which intuitively measures the cost needed to turn an MST into an Eulerian graph. One of our main algorithmic contributions is to show that this quantity can be meaningfully estimated by a sublinear number of queries in the query model. On one hand, the fact that a metric stream reveals pairwise distances for all pairs of vertices provably helps algorithmically. On the other hand, it also seems to render useless techniques for proving space lower bounds via reductions from well-known hard communication problems. Our main technical contribution in lower bounds is to identify and characterize the communication complexity of new problems that can serve as canonical starting point for proving metric stream lower bounds.

In the metric Steiner Tree problem, we are given an n × n metric w on a set V of vertices along with a set T ⊆ V of k terminals, and the goal is to find a tree of minimum cost that contains all terminals in T. This is a well-known NP-hard problem and much of the previous work has focused on understanding its polynomial-time approximability. In this work, we initiate a study of the query complexity of the metric Steiner Tree problem. Specifically, if we desire an α-approximate estimate of the metric Steiner Tree cost, how many entries need to be queried in the metric w? For the related minimum spanning tree (MST) problem, this question is well-understood. For any fixed ε > 0, one can estimate the MST cost to within a (1 + ε)-factor using only Õ(n) queries, and this is known to be essentially tight. Can one obtain a similar result for Steiner Tree cost? Note that a (2 + ε)-approximate estimate of Steiner Tree cost can be obtained with Õ(k) queries by simply applying the MST cost estimation algorithm on the metric induced by the terminals. Our first result shows that the Steiner Tree problem behaves in a fundamentally different manner from MST: any (randomized) algorithm that estimates the Steiner Tree cost to within a (5/3 — ε)-factor requires Ω(n2) queries, even if k is a constant. This lower bound is in sharp contrast to an upper bound of O(nk) queries for computing a (5/3)-approximate Steiner Tree, which follows from previous work by Du and Zelikovsky. Our second main result, and the main technical contribution of this work, is a sublinear query algorithm for estimating the Steiner Tree cost to within a strictly better-than-2 factor. We give an algorithm that achieves this goal, with a query complexity of Õ(n12/7 + n6/7 · k); since k ≤ n, the algorithm performs at most O(n13/7) = o(n2) queries in the worst-case. Our estimation algorithm reduces this task to that of designing a sublinear query algorithm for a suitable set cover problem. We complement this result by showing an query lower bound for any algorithm that estimates Steiner Tree cost to a strictly better than 2 factor. Thus queries are needed to just beat 2-approximation when k = Ω(n); a sharp contrast to MST cost estimation where a (1 + o(1))-approximate estimate of cost is achievable with only Õ(n) queries. * The full version of the paper can be accessed at https://arxiv.org/abs/2211.03893

A seminal work of [Ahn-Guha-McGregor, PODS’12] showed that one can compute a cut sparsifier of an unweighted undirected graph by taking a near-linear number of linear measurements on the graph. Subsequent works also studied computing other graph sparsifiers using linear sketching, and obtained near-linear upper bounds for spectral sparsifiers [Kapralov-Lee-Musco-Musco-Sidford, FOCS’14] and first non-trivial upper bounds for spanners [Filtser-Kapralov-Nouri, SODA’21]. All these linear sketching algorithms, however, only work on unweighted graphs, and are extended to weighted graphs by weight grouping, a non-linear operation not implementable in, for instance, general turnstile streams.In this paper, we initiate the study of weighted graph sparsification by linear sketching by investigating a natural class of linear sketches that we call incidence sketches, in which each measurement is a linear combination of the weights of edges incident on a single vertex. This class captures all aforementioned linear sketches for unweighted sparsification. It also covers linear sketches implementable in the simultaneous communication model, where edges are distributed across n machines. Our results are:1)Weighted cut sparsification: We give an algorithm that computes a $(1+\epsilon)$-cut sparsifier using $\tilde{O}(n\epsilon^{-3})$ linear measurements, which is nearly optimal. This also implies a turnstile streaming algorithm with $\tilde{O}(n\epsilon^{-3})$ space. Our algorithm is achieved by building a so-called “weighted edge sampler” for each vertex.2)Weighted spectral sparsification: We give an algorithm that computes a $(1+\epsilon)$-spectral sparsifier using $\tilde{O}(n^{6/5}\epsilon^{-4})$ linear measurements. This also implies a turnstile streaming algorithm with $\tilde{O}(n^{6/5}\epsilon^{-4})$ space. Key to our algorithm is a novel analysis of how the effective resistances change under vertex sampling. Complementing our algorithm, we then prove a superlinear lower bound of $\Omega(n^{21/20-o(1)})$ measurements for computing some O(1)-spectral sparsifier using incidence sketches.3)Weighted spanner computation: We first show that any $o(n^{2})$ linear measurements can only recover a spanner of stretch that in general depends linearly on $\frac{w_{\max}}{w_{\min}}$. We thus focus on graphs with $\frac{w_{\max}}{w_{\min}}=O(1)$ and study the stretch’s dependence on n. On such graphs, the algorithm in [FiltserKapralov-Nouri, SODA’21] can obtain a spanner of stretch $\tilde{O}\left(n^{\frac{2}{3}\left(1-\alpha\right)}\right)$ using $\tilde{O}(n^{1+\alpha})$ measurements for any $\alpha\in [0,1]$. We prove that, for incidence sketches, this tradeoff is optimal up to an $n^{o(1)}$ factor for all $\alpha\lt 1/10$.We prove both our lower bounds by analyzing the “effective resistances” in certain matrix-weighted graphs, where we develop a number of new tools for reasoning about such graphs – most notably (i) a matrix-weighted analog of the widely used expander decomposition of ordinary graphs, and (ii) a proof that a random vertex-induced subgraph of a matrix-weighted expander is also an expander. We believe these tools are of independent interest.

The problem of minimizing a submodular function (SFM) is a common generalization of several fundamental combinatorial optimization problems, including minimum$s-t$cuts in graphs and matroid intersection. It is well-known that a submodular function can be minimized with only$\text{poly} (N)$function evaluation queries where$N$denotes the universe size. However, all known polynomial query algorithms for SFM are highly adaptive, requiring at least$N$rounds of adaptivity. A natural question is if SFM can be efficiently solved in a highly parallel manner, namely, with$\text{poly} (N)$queries using only poly-logarithmic rounds of adaptivity. An important step towards understanding the adaptivity needed to solve SFM efficiently was taken in the very recent work of Balkanski and Singer who showed that any SFM algorithm with$\text{poly} (N)$queries. This left open the possibility of efficient SFM algorithms with poly-logarithmic rounds of adaptivity. In this work, we strongly rule out this possibility by showing that any, possibly randomized, algorithm for submodular function minimization making$\text{poly} (N)$queries requires$\tilde{\Omega}(N^{1/3})$rounds of adaptivity. In fact, we show a polynomial lower bound on the number of rounds of adaptivity even for algorithms that make up to$2^{N^{1-\delta}}$queries, for any constant$\delta > 0$.

The problem of sparsifying a graph or a hypergraph while approximately preserving its cut structure has been extensively studied and has many applications. In a seminal work, Benczúr and Karger (1996) showed that given any n-vertex undirected weighted graph G and a parameter ε ∈ (0,1), there is a near-linear time algorithm that outputs a weighted subgraph G' of G of size Õ(n/ε²) such that the weight of every cut in G is preserved to within a (1 ± ε)-factor in G'. The graph G' is referred to as a (1 ± ε)-approximate cut sparsifier of G. Subsequent recent work has obtained a similar result for the more general problem of hypergraph cut sparsifiers. However, all known sparsification algorithms require Ω(n + m) time where n denotes the number of vertices and m denotes the number of hyperedges in the hypergraph. Since m can be exponentially large in n, a natural question is if it is possible to create a hypergraph cut sparsifier in time polynomial in n, independent of the number of edges. We resolve this question in the affirmative, giving the first sublinear time algorithm for this problem, given appropriate query access to the hypergraph. Specifically, we design an algorithm that constructs a (1 ± ε)-approximate cut sparsifier of a hypergraph H(V,E) in polynomial time in n, independent of the number of hyperedges, when given access to the hypergraph using the following two queries: 1) given any cut (S, ̄S), return the size |δ_E(S)| (cut value queries); and 2) given any cut (S, ̄S), return a uniformly at random edge crossing the cut (cut edge sample queries). Our algorithm outputs a sparsifier with Õ(n/ε²) edges, which is essentially optimal. We then extend our results to show that cut value and cut edge sample queries can also be used to construct hypergraph spectral sparsifiers in poly(n) time, independent of the number of hyperedges. We complement the algorithmic results above by showing that any algorithm that has access to only one of the above two types of queries can not give a hypergraph cut sparsifier in time that is polynomial in n. Finally, we show that our algorithmic results also hold if we replace the cut edge sample queries with a pair neighbor sample query that for any pair of vertices, returns a random edge incident on them. In contrast, we show that having access only to cut value queries and queries that return a random edge incident on a given single vertex, is not sufficient.

Cuts in graphs are a fundamental object of study, and play a central role in the study of graph algorithms. The problem of sparsifying a graph while approximately preserving its cut structure has been extensively studied and has many applications. In a seminal work, Benczúr and Karger (1996) showed that given any n-vertex undirected weighted graph G and a parameter ε ∈ (0,1), there is a near-linear time algorithm that outputs a weighted subgraph G' of G of size Õ(n/ε2) such that the weight of every cut in G is preserved to within a ( 1±ε)-factor in G'. The graph G' is referred to as a ( 1±ε)-approximate cut sparsifier of G. A natural question is if such cut-preserving sparsifiers also exist for hypergraphs. Kogan and Krauthgamer (2015) initiated a study of this question and showed that given any weighted hypergraph H where the cardinality of each hyperedge is bounded by r, there is a polynomial-time algorithm to find a ( 1±ε)-approximate cut sparsifier of H of size Õ([nr/(ε2)]). Since r can be as large as n, in general, this gives a hypergraph cut sparsifier of size Õ(n2/ε2), which is a factor n larger than the Benczúr-Karger bound for graphs. It has been an open question whether or not Benczúr-Karger bound is achievable on hypergraphs. In this work, we resolve this question in the affirmative by giving a new polynomial-time algorithm for creating hypergraph sparsifiers of size Õ(n/ε2).

We consider the problem of designing sublinear time algorithms for estimating the cost of a minimum metric traveling salesman (TSP) tour. Specifically, given access to a $n \times n$ distance matrix $D$ that specifies pairwise distances between $n$ points, the goal is to estimate the TSP cost by performing only sublinear (in the size of $D$) queries. For the closely related problem of estimating the weight of a metric minimum spanning tree (MST), it is known that for any $\varepsilon > 0$, there exists an $\tilde{O}(n/\varepsilon^{O(1)})$ time algorithm that returns a $(1 + \varepsilon)$-approximate estimate of the MST cost. This result immediately implies an $\tilde{O}(n/\varepsilon^{O(1)})$ time algorithm to estimate the TSP cost to within a $(2 + \varepsilon)$ factor for any $\varepsilon > 0$. However, no $o(n^2)$ time algorithms are known to approximate metric TSP to a factor that is strictly better than $2$. On the other hand, there were also no known barriers that rule out the existence of $(1 + \varepsilon)$-approximate estimation algorithms for metric TSP with $\tilde{O}(n)$ time for any fixed $\varepsilon > 0$. In this paper, we make progress on both algorithms and lower bounds for estimating metric TSP cost. We also show that the problem of estimating metric TSP cost is closely connected to the problem of estimating the size of a maximum matching in a graph.

Real-time decision making in IoT applications relies upon space-efficient evaluation of queries over streaming data. To model the uncertainty in the classification of data being processed, we consider the model of probabilistic strings --- sequences of discrete probability distributions over a finite set of events, and initiate the study of space complexity of streaming computation for different classes of queries over such probabilistic strings.

Suppose a graph G is stochastically created by uniformly sampling vertices along a line segment and connecting each pair of vertices with a probability that is a known decreasing function of their distance. We ask if it is possible to reconstruct the actual positions of the vertices in G by only observing the generated unlabeled graph. We study this question for two natural edge probability functions — one where the probability of an edge decays exponentially with the distance and another where this probability decays only linearly. We initiate our study with the weaker goal of recovering only the order in which vertices appear on the line segment. For a segment of length n and a precision parameter δ, we show that for both exponential and linear decay edge probability functions, there is an efficient algorithm that correctly recovers (up to reflection symmetry) the order of all vertices that are at least δ apart, using only samples (vertices). Building on this result, we then show that vertices (samples) are sufficient to additionally recover the location of each vertex on the line to within a precision of δ. We complement this result with an lower bound on samples needed for reconstructing positions (even by a computationally unbounded algorithm), showing that the task of recovering positions is information-theoretically harder than recovering the order. We give experimental results showing that our algorithm recovers the positions of almost all points with high accuracy.

We study a network formation game where agents receive benefits by forming connections to other agents but also incur both direct and indirect costs from the formed connections. Specifically, once the agents have purchased their connections, an attack starts at a randomly chosen vertex in the network and spreads according to the independent cascade model with a fixed probability, destroying any infected agents. The utility or welfare of an agent in our game is defined to be the expected size of the agent's connected component post-attack minus her expenditure in forming connections. Our goal is to understand the properties of the equilibrium networks formed in this game. Our first result concerns the edge density of equilibrium networks. A network connection increases both the likelihood of remaining connected to other agents after an attack as well the likelihood of getting infected by a cascading spread of infection. We show that the latter concern primarily prevails and any equilibrium network in our game contains only $O(n\log n)$ edges where $n$ denotes the number of agents. On the other hand, there are equilibrium networks that contain $\Omega(n)$ edges showing that our edge density bound is tight up to a logarithmic factor. Our second result shows that the presence of attack and its spread through a cascade does not significantly lower social welfare as long as the network is not too dense. We show that any non-trivial equilibrium network with $O(n)$ edges has $\Theta(n^2)$ social welfare, asymptotically similar to the social welfare guarantee in the game without any attacks.

Any graph with maximum degree Δ admits a proper vertex coloring with Δ+1 colors that can be found via a simple sequential greedy algorithm in linear time and space. But can one find such a coloring via a sublinear algorithm? We answer this fundamental question in the affirmative for several canonical classes of sublinear algorithms including graph streaming, sublinear time, and massively parallel computation (MPC) algorithms. In particular, we design: A single-pass semi-streaming algorithm in dynamic streams using Õ(n) space. The only known semi-streaming algorithm prior to our work was a folklore O(log n)-pass algorithm obtained by simulating classical distributed algorithms in the streaming model. A sublinear-time algorithm in the standard query model that allows neighbor queries and pair queries using time. We further show that any algorithm that outputs a valid coloring with sufficiently large constant probability requires time. No non-trivial sublinear time algorithms were known prior to our work. A parallel algorithm in the massively parallel computation (MPC) model using Õ(n) memory per machine and O(1) MPC rounds. Our number of rounds significantly improves upon the recent O(log log Δ · log* (n))-round algorithm of Parter [ICALP 2018]. At the core of our results is a remarkably simple meta-algorithm for the (Δ + 1) coloring problem: Sample O(log n) colors for each vertex independently and uniformly at random from the Δ + 1 colors; find a proper coloring of the graph using only the sampled colors of each vertex. As our main result, we prove that the sampled set of colors with high probability contains a proper coloring of the input graph. The sublinear algorithms are then obtained by designing efficient algorithms for finding a proper coloring of the graph from the sampled colors in each model. We note that all our upper bound results for (Δ + 1) coloring are either optimal or close to best possible in each model studied. We also establish new lower bounds that rule out the possibility of achieving similar results in these models for the closely related problems of maximal independent set and maximal matching. Collectively, our results highlight a sharp contrast between the complexity of (Δ+1) coloring vs maximal independent set and maximal matching in various models of sublinear computation even though all three problems are solvable by a simple greedy algorithm in the classical setting.

We present new lower bounds that show that a polynomial number of passes are necessary for solving some fundamental graph problems in the streaming model of computation. For instance, we show that any streaming algorithm that finds a weighted minimum s-t cut in an n-vertex undirected graph requires n2−o(1) space unless it makes nΩ(1) passes over the stream.