Improved Distributed Expander Decomposition and Nearly Optimal Triangle Enumeration

Yi-Jun Chang, Thatchaphol Saranurak
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引用次数: 39

Abstract

An(ε,φ)-expander decomposition of a graph G=(V,E) is a clustering of the vertices V=V1∪…∪ Vx such that (1) each cluster Vi induces subgraph with conductance at least φ, and (2) the number of inter-cluster edges is at most ε|E|. In this paper, we give an improved distributed expander decomposition, and obtain a nearly optimal distributed triangle enumeration algorithm in the CONGEST model. Specifically, we construct an (ε,φ)-expander decomposition with φ=(ε/log n)2 O(k) in O(n2/k ⋅ poly (1/φ, log n))rounds for any ε ∈(0,1) and positive integer k. For example, a (1/no(1), 1/no(1))-expander decomposition only requires O(no(1)) rounds to compute, which is optimal up to subpolynomial factors, and a (0.01,1/poly log n)-expander decomposition can be computed in O(nγ) rounds, for any arbitrarily small constant γ > 0. Previously, the algorithm by Chang, Pettie, and Zhang can construct a (1/6,1/poly log n)-expander decomposition using Õ (n1-δ) rounds for any δ > 0, with a caveat that the algorithm is allowed to throw away a set of edges into an extra part which form a subgraph with arboricity at most nδ. 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 Õ(n1/3) rounds. This matches the lower bound by Izumi and LeGall [PODC'17] and Pandurangan, Robinson and Scquizzato [SPAA'18] of Ø(n1/3) which holds even in the CONGESTED-CLIQUE model. To the best of our knowledge, 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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改进的分布扩展器分解和近最优三角枚举
图G=(V,E)的(ε,φ)展开分解是顶点V=V1∪…∪Vx的聚类,满足(1)每个聚类Vi诱导电导至少为φ的子图,(2)聚类间边的数目最多为ε|E|。本文给出了一种改进的分布展开器分解方法,并在CONGEST模型下得到了一种近似最优的分布三角形枚举算法。具体地说,对于任何ε∈(0,1)和正整数k,我们构造了一个φ=(ε/log n) 2o (k)的(ε,φ)-扩展分解,在O(n2/k·poly (1/φ, log n))轮中。例如,a (1/no(1), 1/no(1))-扩展分解只需要O(no(1))轮计算,这是最优的次多项式因子,对于任意小常数γ > 0, a (0.01,1/poly log n)-扩展分解可以在O(nγ)轮中计算。以前,Chang, Pettie和Zhang的算法可以使用Õ (n1-δ)轮对任意δ > 0构造(1/6,1/poly log n)-展开器分解,但需要注意的是,该算法允许将一组边丢弃到额外的部分中,这些边形成一个最多为nδ的任意子图。我们的算法没有这个警告。通过对Ghaffari、Kuhn和Su [PODC'17]在扩展机上路由的分布式算法稍加修改,我们得到了一个使用Õ(n1/3)轮的三角形枚举算法。这与Izumi和LeGall [PODC'17]以及Pandurangan、Robinson和Scquizzato [SPAA'18]在Ø(n1/3)中的下界相匹配,即使在congeded - clique模型中也是如此。据我们所知,这为在CONGEST和congeded - clique中具有本质上相同复杂性(直到一个多对数因子)的分布式问题提供了第一个非平凡的示例。我们证明的关键技术是第一个分布式近似算法,用于寻找尽可能平衡的低电导切割。以前的分布式稀疏切算法没有这种几乎最平衡的保证。
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