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Improved Distributed Expander Decomposition and Nearly Optimal Triangle Enumeration

Published 17 Apr 2019 in cs.DS and cs.DC | (1904.08037v2)

Abstract: An (ϵ,ϕ)(\epsilon,\phi)-expander decomposition of a graph G=(V,E)G=(V,E) is a clustering of the vertices V=V1VxV=V_{1}\cup\cdots\cup V_{x} such that (1) each cluster ViV_{i} induces subgraph with conductance at least ϕ\phi, and (2) the number of inter-cluster edges is at most ϵE\epsilon|E|. In this paper, we give an improved distributed expander decomposition. Specifically, we construct an (ϵ,ϕ)(\epsilon,\phi)-expander decomposition with ϕ=(ϵ/logn)<sup>2<sup>O(k)\phi=(\epsilon/\log n)<sup>{2<sup>{O(k)}} in O(n<sup>2/kpoly(1/ϕ,log</sup>n))O(n<sup>{2/k}\cdot\text{poly}(1/\phi,\log</sup> n)) rounds for any ϵ(0,1)\epsilon\in(0,1) and positive integer kk. For example, a (0.01,1/polylogn)(0.01,1/\text{poly}\log n)-expander decomposition can be computed in O(n<sup>γ)O(n<sup>{\gamma}) rounds, for any arbitrarily small constant $\gamma&gt;0$. Previously, the algorithm by Chang, Pettie, and Zhang can construct a (1/6,1/polylogn)(1/6,1/\text{poly}\log n)-expander decomposition using O~(n<sup>1δ)\tilde{O}(n<sup>{1-\delta}) rounds for any $\delta&gt;0$, with a caveat that the algorithm is allowed to throw away a set of edges into an extra part which forms a subgraph with arboricity at most n<sup>δn<sup>{\delta}. Our algorithm does not have this caveat. By slightly modifying the distributed algorithm for routing on expanders by Ghaffari, Kuhn and Su [PODC'17], we obtain a triangle enumeration algorithm using O~(n<sup>1/3)\tilde{O}(n<sup>{1/3}) rounds. This matches the lower bound by Izumi and Le Gall [PODC'17] and Pandurangan, Robinson and Scquizzato [SPAA'18] of Ω~(n<sup>1/3)\tilde{\Omega}(n<sup>{1/3}) which holds even in the CONGESTED CLIQUE model. This provides the first non-trivial example for a distributed problem that has essentially the same complexity (up to a polylogarithmic factor) in both CONGEST and CONGESTED CLIQUE. The key technique in our proof is the first distributed approximation algorithm for finding a low conductance cut that is as balanced as possible. Previous distributed sparse cut algorithms do not have this nearly most balanced guarantee.

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