Community detection in networks with positive and negative links

TL;DR

This paper introduces a novel community detection method for networks with both positive and negative links, extending existing models to better analyze complex social and international networks.

Contribution

It extends the Potts model to incorporate negative links, enabling more accurate detection of communities in signed networks, such as international alliances and disputes.

Findings

Identified six global power blocs similar to Huntington's civilizations

Demonstrated the method on international alliance data from 1993-2001

Revealed notable exceptions in community structures

Abstract

Detecting communities in complex networks accurately is a prime challenge, preceding further analyses of network characteristics and dynamics. Until now, community detection took into account only positively valued links, while many actual networks also feature negative links. We extend an existing Potts model to incorporate negative links as well, resulting in a method similar to the clustering of signed graphs, as dealt with in social balance theory, but more general. To illustrate our method, we applied it to a network of international alliances and disputes. Using data from 1993--2001, it turns out that the world can be divided into six power blocs similar to Huntington's civilizations, with some notable exceptions.