A New Randomized Algorithm for Community Detection in Large Networks

Abstract

The problem of community detection (or clustering) in graphs plays an important role in analysis of complex large-scale networks and big data structures, arising in natural, behavioral and engineering sciences. Examples of such networks include, but are not limited to, World Wide Web (WWW) and Internet, social networks, ecological networks and food webs, cellular and molecular ensembles. A community (or a module) in a graph is a subset of its nodes, whose members are "densely" connected to each other yet have relatively few connections with nodes outside this subset. A number of algorithms to subdivide the nodes of large-scale graphs into communities have recently been proposed; many of them hunt for the graph’s partitions of maximal modularity. One of the most efficient graph clustering algorithms of this type is the Multi-Level Aggregation (or "Louvain") method. In this paper, a randomized counterpart of this algorithm is proposed, which provides a comparable "quality" of graph’s clustering, being however much faster on huge graphs. We demonstrate the efficiency of our algorithm, comparing its performance on several "benchmark" large-scale graphs with existing methods.