Advances in scaling community discovery methods for signed graph networks

Abstract

AbstractCommunity detection is a common task in social network analysis with applications in a variety of fields including medicine, criminology and business. Despite the popularity of community detection, there is no clear consensus on the most effective methodology for signed networks. In this article, we summarize the development of community detection in signed networks and evaluate current state-of-the-art techniques on several real-world datasets. First, we give a comprehensive background of community detection in signed graphs. Next, we compare various adaptations of the Laplacian matrix in recovering ground-truth community labels via spectral clustering in small signed graph datasets. Then, we evaluate the scalability of leading algorithms on small, large, dense and sparse real-world signed graph networks. We conclude with a discussion of our novel findings and recommendations for extensions and improvements in state-of-the-art techniques for signed graph community discovery in real-world signed graphs.