A Compressive Sensing Approach to Community Detection with Applications

TL;DR

This paper introduces a novel compressive sensing-based algorithm for community detection in graphs, capable of efficiently identifying individual clusters with theoretical guarantees and outperforming traditional spectral clustering in speed.

Contribution

The paper develops a two-stage compressive sensing approach for community detection that efficiently finds individual clusters and provides robustness guarantees under the Stochastic Block Model.

Findings

Algorithm finds a single cluster in O(nlog(n)n_0) operations.

All k clusters can be found in fewer than O(n^2ln(n)) operations.

The method outperforms spectral clustering in speed and provides robustness guarantees.

Abstract

The community detection problem for graphs asks one to partition the n vertices V of a graph G into k communities, or clusters, such that there are many intracluster edges and few intercluster edges. Of course this is equivalent to finding a permutation matrix P such that, if A denotes the adjacency matrix of G, then PAP^T is approximately block diagonal. As there are k^n possible partitions of n vertices into k subsets, directly determining the optimal clustering is clearly infeasible. Instead one seeks to solve a more tractable approximation to the clustering problem. In this paper we reformulate the community detection problem via sparse solution of a linear system associated with the Laplacian of a graph G and then develop a two-stage approach based on a thresholding technique and a compressive sensing algorithm to find a sparse solution which corresponds to the community containing a vertex of interest in G. Crucially, our approach results in an algorithm which is able to find a single cluster of size n_0 in O(nlog(n)n_0) operations and all k clusters in fewer than O(n^2ln(n)) operations. This is a marked improvement over the classic spectral clustering algorithm, which is unable to find a single cluster at a time and takes approximately O(n^3) operations to find all k clusters. Moreover, we are able to provide robust guarantees of success for the case where G is drawn at random from the Stochastic Block Model, a popular model for graphs with clusters. Extensive numerical results are also provided, showing the efficacy of our algorithm on both synthetic and real-world data sets.