PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Multi-agent random walks for local clustering
Morteza Alamgir and Ulrike v. Luxburg
In: ICDM(2010).

Abstract

We consider the problem of local graph clustering where the aim is to discover the local cluster corresponding to a point of interest. The most popular algorithms to solve this problem start a random walk at the point of interest and let it run until some stopping criterion is met. The vertices visited are then considered the local cluster. We suggest a more powerful alternative, the multi-agent random walk. It consists of several “agents” connected by a fixed rope of length l. All agents move independently like a standard random walk on the graph, but they are constrained to have distance at most l from each other. The main insight is that for several agents it is harder to simultaneously travel over the bottleneck of a graph than for just one agent. Hence, the multi-agent random walk has less tendency to mistakenly merge two different clusters than the original random walk. In our paper we analyze the multi-agent random walk theoretically and compare it experimentally to the major local graph clustering algorithms from the literature. We find that our multi-agent random walk consistently outperforms these algorithms.

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EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Theory & Algorithms
ID Code:7276
Deposited By:Ulrike Von Luxburg
Deposited On:16 March 2011