Known by the company we keep: `Triadic influence' as a proxy for compatibility in social relationships

TL;DR

This study introduces the 'triadic influence' metric to predict social relationships in school networks, demonstrating its effectiveness over complex embeddings and highlighting its potential to understand social network evolution.

Contribution

The paper presents a novel, simple metric called 'triadic influence' that outperforms high-dimensional embeddings in predicting social ties, advancing quantitative analysis of social networks.

Findings

Triadic influence achieves highest prediction accuracy.

Neural networks effectively model relationship probabilities.

Triadic influence correlates with relationship dynamics.

Abstract

Networks of social interactions are the substrate upon which civilizations are built. Often, we create new bonds with people that we like or feel that our relationships are damaged through the intervention of third parties. Despite their importance and the huge impact that these processes have in our lives, quantitative scientific understanding of them is still in its infancy, mainly due to the difficulty of collecting large datasets of social networks including individual attributes. In this work, we present a thorough study of real social networks of 13 schools, with more than 3,000 students and 60,000 declared positive and negative relations, including tests for personal traits of all the students. We introduce a metric -- the `triadic influence' -- that measures the influence of nearest-neighbors in the relationships of their contacts. We use neural networks to predict the relationships and to extract the probability that two students are friends or enemies depending on their personal attributes or the triadic influence. We alternatively use a high-dimensional embedding of the network structure to also predict the relationships. Remarkably, the triadic influence (a simple one-dimensional metric) achieves the highest accuracy at predicting the relationship between two students. We postulate that the probabilities extracted from the neural networks -- functions of the triadic influence and the personalities of the students -- control the evolution of real social networks, opening a new avenue for the quantitative study of these systems.