Flow-Based Algorithms for Local Graph Clustering

Given a subset A of vertices of an undirected graph G, the cut-improvement problem asks us to find a subset S that is similar to A but has smaller conductance. An elegant algorithm for this problem has been given by Andersen and Lang [AL08] and requires solving a small number of single-commodity maximum flow computations over the whole graph G. In this paper, we introduce LocalImprove, the first cut-improvement algorithm that is local, i.e., that runs in time dependent on the size of the input set A rather than on the size of the entire graph. Moreover, LocalImprove achieves this local behavior while closely matching the same theoretical guarantee as the global algorithm of Andersen and Lang. The main application of LocalImprove is to the design of better local-graph-partitioning algorithms. All previously known local algorithms for graph partitioning are random-walk based and can only guarantee an output conductance of Õ( √ φopt) when the target set has conductance φopt ∈ [0, 1]. Very recently, Zhu, Lattanzi and Mirrokni [ZLM13] improved this to O(φopt/ √ Conn) where the internal connectivity parameter Conn ∈ [0, 1] is defined as the reciprocal of the mixing time of the random walk over the graph induced by the target set. This regime is of high practical interest in learning applications as it corresponds to the case when the target set is a well-connected ground-truth cluster. In this work, we show how to use LocalImprove to obtain a constant approximation O(φopt) as long as Conn/φopt = Ω(1). This yields the first flow-based algorithm for local graph partitioning. Moreover, its performance strictly outperforms the ones based on random walks and surprisingly matches that of the best known global algorithm, which is SDP-based, in this parameter regime [MMV12]. Finally, our results show that spectral methods are not the only viable approach to the construction of local graph partitioning algorithm and open door to the study of algorithms with even better approximation and locality guarantees.