Understanding friendship formation with explainable machine learning

TL;DR

This study uses explainable machine learning to analyze social tie formation, revealing that local network structure mainly drives relationships, with individual traits explaining only a small subset of links.

Contribution

It demonstrates the effectiveness of interpretable ML models in uncovering the dominant role of network structure over individual traits in social tie formation.

Findings

Triadic influence dominates link prediction.

A small subset (0.24%) of links is explained by individual traits.

Links explained by traits tend to be weaker, less embedded, and more negative.

Abstract

Understanding the formation of social ties requires disentangling the roles of individual traits and local network structure. We analyse signed social relationships among 3,395 students using an interpretable machine learning model -- the Explainable Boosting Machine (EBM) -- to predict link polarity from individual attributes (prosociality, cognitive reflection, and gender) and a structural metric, triadic influence. Our results show that triadic influence overwhelmingly dominates link prediction, confirming that local network structure is the primary driver of social relationships. Nevertheless, a small subset of links (0.24\%) is primarily explained by individual-level traits. A detailed characterisation of this subset reveals that these links do not arise from distinct structural conditions, but rather correspond to weaker and less structurally embedded relationships. In particular, they are more likely to be negative ties and exhibit lower levels of structural balance, whereas triadic-dominant links are strongly associated with positive relationships and highly balanced configurations. Furthermore, we find that links without indirect structural paths are not explained by individual traits, but by the absence of structural reinforcement itself. These findings support a layered view of social tie formation, in which structural mechanisms dominate globally, while individual-level effects emerge in specific, less constrained contexts. More broadly, our work highlights the value of explainable machine learning for uncovering the mechanisms underlying social network formation.