How Homophily Affects Diffusion and Learning in Networks

TL;DR

This paper investigates how homophily influences information diffusion and learning in social networks, revealing that it significantly impacts certain processes like averaging and random walks but not broadcast or shortest path communication.

Contribution

It provides new theoretical insights into the spectral effects of homophily on diffusion processes and validates these predictions with real-world social network data.

Findings

Homophily slows learning based on averaging and Markovian diffusion.

Connection density has no effect on homophily's impact in certain processes.

Theoretical spectral results are confirmed with empirical high school friendship networks.

Abstract

We examine how three different communication processes operating through social networks are affected by homophily -- the tendency of individuals to associate with others similar to themselves. Homophily has no effect if messages are broadcast or sent via shortest paths; only connection density matters. In contrast, homophily substantially slows learning based on repeated averaging of neighbors' information and Markovian diffusion processes such as the Google random surfer model. Indeed, the latter processes are strongly affected by homophily but completely independent of connection density, provided this density exceeds a low threshold. We obtain these results by establishing new results on the spectra of large random graphs and relating the spectra to homophily. We conclude by checking the theoretical predictions using observed high school friendship networks from the Adolescent Health dataset.