The Anatomy of the Facebook Social Graph

TL;DR

This paper analyzes Facebook's social graph, revealing its near-complete connectivity, dense local neighborhoods, and demographic-based community structures, providing insights into large-scale social network properties.

Contribution

It offers a comprehensive analysis of Facebook's social graph, highlighting its global connectivity, local density, and demographic assortativity, which were less understood in prior smaller-scale studies.

Findings

99.91% of users in a single connected component

Social neighborhoods are surprisingly dense despite overall sparsity

Clear degree and age-based assortativity patterns

Abstract

We study the structure of the social graph of active Facebook users, the largest social network ever analyzed. We compute numerous features of the graph including the number of users and friendships, the degree distribution, path lengths, clustering, and mixing patterns. Our results center around three main observations. First, we characterize the global structure of the graph, determining that the social network is nearly fully connected, with 99.91% of individuals belonging to a single large connected component, and we confirm the "six degrees of separation" phenomenon on a global scale. Second, by studying the average local clustering coefficient and degeneracy of graph neighborhoods, we show that while the Facebook graph as a whole is clearly sparse, the graph neighborhoods of users contain surprisingly dense structure. Third, we characterize the assortativity patterns present in the graph by studying the basic demographic and network properties of users. We observe clear degree assortativity and characterize the extent to which "your friends have more friends than you". Furthermore, we observe a strong effect of age on friendship preferences as well as a globally modular community structure driven by nationality, but we do not find any strong gender homophily. We compare our results with those from smaller social networks and find mostly, but not entirely, agreement on common structural network characteristics.