Homophily and Triadic Closure in Evolving Social Networks

TL;DR

This paper introduces a dynamic network model that incorporates multidimensional assortativity, triadic closure, and evolving features, validated through simulations and empirical analysis of scientific collaboration networks.

Contribution

It presents a novel stochastic model for evolving social networks that accounts for feature-based assortativity and triadic closure without fixing the total feature set.

Findings

Model accurately captures network growth dynamics

Statistical estimators effectively infer model parameters

Empirical validation on scientific collaboration network

Abstract

We present a new network model accounting for multidimensional assortativity. Each node is characterized by a number of features and the probability of a link between two nodes depends on common features. We do not fix a priori the total number of possible features. The bipartite network of the nodes and the features evolves according to a stochastic dynamics that depends on three parameters that respectively regulate the preferential attachment in the transmission of the features to the nodes, the number of new features per node, and the power-law behavior of the total number of observed features. Our model also takes into account a mechanism of triadic closure. We provide theoretical results and statistical estimators for the parameters of the model. We validate our approach by means of simulations and an empirical analysis of a network of scientific collaborations.