Testing structural balance theories in heterogeneous signed networks

TL;DR

This paper evaluates structural balance theories in signed social networks by extending exponential random graph models to account for heterogeneity, revealing that the validity of balance theories depends on the null model used.

Contribution

It introduces a novel extension of exponential random graph models for signed networks with heterogeneity, enabling more accurate assessment of balance theory in social data.

Findings

Heterogeneous benchmarks support strong balance theory.

Homogeneous benchmarks support weak balance theory.

Biological networks show persistent frustration regardless of the null model.

Abstract

The abundance of data about social relationships allows the human behavior to be analyzed as any other natural phenomenon. Here we focus on balance theory, stating that social actors tend to avoid establishing cycles with an odd number of negative links. This statement, however, can be supported only after a comparison with a benchmark. Since the existing ones disregard actors' heterogeneity, we extend Exponential Random Graphs to signed networks with both global and local constraints and employ them to assess the significance of empirical unbalanced patterns. We find that the nature of balance crucially depends on the null model: while homogeneous benchmarks favor the weak balance theory, according to which only triangles with one negative link should be under-represented, heterogeneous benchmarks favor the strong balance theory, according to which also triangles with all negative links should be under-represented. Biological networks, instead, display strong frustration under any benchmark, confirming that structural balance inherently characterizes social networks.