We present an exact fully-dynamic minimum cut algorithm that runs in \(n^{o(1)}\) deterministic update time when the minimum cut size is at most \(2^{\Theta(\log^{3/4-c} n)}\) for any \(c \gt 0\), improving on the previous algorithm of Jin, Sun, and Thorup (SODA 2024) whose minimum cut size limit is \((\log n)^{o(1)}\). Combined with graph sparsification, we obtain the first \((1+\epsilon)\)-approximate fully-dynamic minimum cut algorithm on weighted graphs, for any \(\epsilon \ge 2^{-\Theta(\log^{3/4-c} n)}\), in \(n^{o(1)}\) randomized update time.

This paper revisits a fundamental distributed computing problem in the population protocol model. Provided n agents each starting with an input color in [k], the relative majority problem asks to find the predominant color. In the population protocol model, at each time step, a scheduler selects two agents that first learn each other's states and then update their states based on what they learned.

We revisit the majority problem in the population protocol communication model, as first studied by Angluin et al. (Distributed Computing 2008). We consider a more general version of this problem known as plurality consensus, which has already been studied intensively in the literature. In this problem, each node in a system of n nodes, has initially one of k different opinions, and they need to agree on the (relative) majority opinion. In particular, we consider the important and intensively studied model of Undecided State Dynamics.

Dynamically maintaining the minimum cut in a graph G under edge insertions and deletion is a fundamental problem in dynamic graph algorithms for which no conditional lower bound on the time per operation exists. In an n-node graph the best known (1 + o (1))-approximate algorithm takes update time [14]. If the minimum cut is guaranteed to be (log n )o (1), a deterministic exact algorithm with n o (1) update time exists [8].

Data dissemination is a fundamental task in distributed computing. This paper studies broadcast problems in various innovative models where the communication network connecting n processes is dynamic (e.g., due to mobility or failures) and controlled by an adversary. In the first model, the processes transitively communicate their ids in synchronous rounds along a rooted tree given in each round by the adversary whose goal is to maximize the number of rounds until at least one id is known by all processes. Previous research has shown a ⌈(3n-1)/2⌉-2 lower bound and an O(nlog log n) upper bound. We show the first linear upper bound for this problem, namely ⌈(1+√2) n-1⌉ ≈ 2.4n. We extend these results to the setting where the adversary gives in each round k-disjoint forests and their goal is to maximize the number of rounds until there is a set of k ids such that each process knows of at least one of them. We give a ⌈3(n-k)/2⌉-1 lower bound and a (π²+6)/6 n+1 ≈ 2.6n upper bound for this problem. Finally, we study the setting where the adversary gives in each round a directed graph with k roots and their goal is to maximize the number of rounds until there exist k ids that are known by all processes. We give a ⌈3(n-3k)/2⌉+2 lower bound and a ⌈(1+√2)n⌉+k-1 ≈ 2.4n+k upper bound for this problem. For the two latter problems no upper or lower bounds were previously known.

We study the broadcast problem on dynamic networks with n processes. The processes communicate in synchronous rounds along an arbitrary rooted tree. The sequence of trees is given by an adversary whose goal is to maximize the number of rounds until at least one process reaches all other processes. Previous research has shown a ⌈(3n-1)/(2)⌉-2 lower bound and an O(n log log n) upper bound. We show the first linear upper bound for this problem, namely ⌈(1 + √2) n-1⌉ ~2.4n. Our result follows from a detailed analysis of the evolution of the adjacency matrix of the network over time.