Sparsity-aware Robust Community Detection(SPARCODE)

TL;DR

SPARCODE is a novel community detection method that enhances robustness and accuracy in sparse, noisy graphs by combining sparsity optimization, outlier detection, and spectral partitioning, outperforming existing approaches.

Contribution

The paper introduces SPARCODE, a new sparsity-aware robust community detection algorithm that effectively handles outliers and noise in sparse graphs, improving detection accuracy and robustness.

Findings

SPARCODE consistently finds the correct number of communities.

It outperforms existing methods in detection performance and modularity score.

The method requires reasonable computation time.

Abstract

Community detection refers to finding densely connected groups of nodes in graphs. In important applications, such as cluster analysis and network modelling, the graph is sparse but outliers and heavy-tailed noise may obscure its structure. We propose a new method for Sparsity-aware Robust Community Detection (SPARCODE). Starting from a densely connected and outlier-corrupted graph, we first extract a preliminary sparsity improved graph model where we optimize the level of sparsity by mapping the coordinates from different clusters such that the distance of their embedding is maximal. Then, undesired edges are removed and the graph is constructed robustly by detecting the outliers using the connectivity of nodes in the improved graph model. Finally, fast spectral partitioning is performed on the resulting robust sparse graph model. The number of communities is estimated using modularity optimization on the partitioning results. We compare the performance to popular graph and cluster-based community detection approaches on a variety of benchmark network and cluster analysis data sets. Comprehensive experiments demonstrate that our method consistently finds the correct number of communities and outperforms existing methods in terms of detection performance, robustness and modularity score while requiring a reasonable computation time.