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تسجيل مستخدم جديدDistributed Triangle Detection via Expander Decomposition
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We present improved distributed algorithms for triangle detection and its variants in the CONGEST model. We show that Triangle Detection, Counting, and Enumeration can be solved in $tilde{O}(n^{1/2})$ rounds. In contrast, the previous state-of-the-art bounds for Triangle Detection and Enumeration were $tilde{O}(n^{2/3})$ and $tilde{O}(n^{3/4})$, respectively, due to Izumi and LeGall (PODC 2017). The main technical novelty in this work is a distributed graph partitioning algorithm. We show that in $tilde{O}(n^{1-delta})$ rounds we can partition the edge set of the network $G=(V,E)$ into three parts $E=E_mcup E_scup E_r$ such that (a) Each connected component induced by $E_m$ has minimum degree $Omega(n^delta)$ and conductance $Omega(1/text{poly} log(n))$. As a consequence the mixing time of a random walk within the component is $O(text{poly} log(n))$. (b) The subgraph induced by $E_s$ has arboricity at most $n^{delta}$. (c) $|E_r| leq |E|/6$. All of our algorithms are based on the following generic framework, which we believe is of interest beyond this work. Roughly, we deal with the set $E_s$ by an algorithm that is efficient for low-arboricity graphs, and deal with the set $E_r$ using recursive calls. For each connected component induced by $E_m$, we are able to simulate congested clique algorithms with small overhead by applying a routing algorithm due to Ghaffari, Kuhn, and Su (PODC 2017) for high conductance graphs.
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