Community detection based on structural balance in signed networks

TL;DR

This paper introduces a motif-based community detection method in signed networks that emphasizes the importance of structural balance, leading to more stable and accurate community identification in both synthetic and real-world networks.

Contribution

It presents a novel motif-based approach that considers the role of negative links in community stability, improving detection accuracy over existing methods.

Findings

Higher performance in community detection on synthetic networks

Strong robustness to network balance variations

Accurate classification in real-world networks

Abstract

In signed networks, some existing community detection methods treat negative connections as intercommunity links and positive ones as intracommunity links. However, it is important to recognize that negative links on real-world networks also play a key role in maintaining community stability. In this work, our aim is to identify communities that are not only densely connected but also harmonious or balanced in terms of the nature of their relationships. Such communities are more likely to be stable over time and less prone to conflicts. Consequently, we propose a motif-based method to identify communities by quantifying the importance of links in the local structural balance. The results in synthetic and real-world networks show that the proposed method has a higher performance in identifying the community. In addition, it demonstrates strong robustness, i.e., remains insensitive to the balance of the network, and accurately classifies communities in real-world networks.