Advances in scaling community discovery methods for signed graph networks

Abstract

Community 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.

Pattern of frustration formation in the functional brain network

“…Considering the brain as a signed network is also a promising approach to investigating the collective behavior of brain regions, where synchronous and antisynchronous coactivations of brain regions determine positive and negative links, respectively (Saberi et al, 2021a) (Figure 1). Signed networks are commonly used for social system modeling in the context of friendship and hostility between entities (Facchetti et al, 2011;Tang et al, 2016;Tomasso et al, 2022;Yang et al, 2007Yang et al, , 2012. Approaches such as the structural balance theory (social balance and social imbalance) are utilized to investigate signed networks for the optimized arrangement of relationships (Alabandi et al, 2021;Anchuri & Magdon-Ismail, 2012;Antal et al, 2005;Bagherikalhor et al, 2021;Derr et al, 2018;Facchetti et al, 2011).…”