Community Detection by Information Flow Simulation

Abstract

Community detection remains an important problem in data mining, owing to the lack of scalable algorithms that exploit all aspects of available data - namely the directionality of flow of information and the dynamics thereof. Most existing methods use measures of connectedness in the graphical structure. In this paper, we present a fast, scalable algorithm to detect communities in directed, weighted graph representations of social networks by simulating flow of information through them. By design, our algorithm naturally handles undirected or unweighted networks as well. Our algorithm runs in $\mathcal{O}(|E|)$ time, which is better than most existing work and uses $\mathcal{O}(|E|)$ space and hence scales easily to very large datasets. Finally, we show that our algorithm outperforms the state-of-the-art Markov Clustering Algorithm (MCL) in both accuracy and scalability on ground truth data (in a number of cases, we can find communities in graphs too large for MCL).

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• Statistical mechanics of complex networks
• Cooperative Game Theory Approaches for Network Partitioning
• The structure of scientific collaboration networks
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• Using Triangles to Improve Community Detection in Directed Networks