Meso-scale structures in signed networks

TL;DR

This paper introduces a new methodology to analyze meso-scale structures in signed networks, revealing prevalent unbalanced structures and complex patterns beyond traditional social balance theory across various real-world networks.

Contribution

The authors develop a methodology that detects unbalanced meso-scale structures in signed networks, overcoming limitations of social balance theory, and apply it to diverse empirical datasets.

Findings

Unbalanced meso-scale structures are common in real-world networks.

Assortativity persists regardless of interaction sign.

Core-periphery structures are typical in online social networks.

Abstract

Meso-scale structures in signed networks have been studied under the limiting assumption of the validity of social balance theory, which predicts positive connections within groups and negative connections between groups. Here, we propose and apply a methodology that overcomes this limitation and is able to find and characterize also the different possible unbalanced structures in signed networks. Applying our methodology to 24 empirical networks, from social-political, financial, and biological domains, we find that unbalanced meso-scale structures are prevalent in real-world networks, including cases with substantial balance at the micro-scale of triangles. In particular, we find that assortativity often prevails regardless of the interaction sign and that core-periphery structures are typical in online social networks. Our findings highlight the complexity of meso-scale relational structures, the importance of using computational methods that are a priori agnostic to specific patterns, and the importance of independently evaluating micro- and meso-scale predictions of social balance theory.