Crawling Facebook for Social Network Analysis Purposes

TL;DR

This paper presents a privacy-compliant web crawler for collecting large-scale Facebook social network data and introduces tools for analyzing graph properties like degree distribution and centrality.

Contribution

It introduces novel, privacy-aware crawling methods and analysis tools for large-scale social network graphs from Facebook data.

Findings

Collected millions of connections in large, anonymous datasets

Analyzed degree distribution and centrality measures

Identified scaling laws and friendship distribution patterns

Abstract

We describe our work in the collection and analysis of massive data describing the connections between participants to online social networks. Alternative approaches to social network data collection are defined and evaluated in practice, against the popular Facebook Web site. Thanks to our ad-hoc, privacy-compliant crawlers, two large samples, comprising millions of connections, have been collected; the data is anonymous and organized as an undirected graph. We describe a set of tools that we developed to analyze specific properties of such social-network graphs, i.e., among others, degree distribution, centrality measures, scaling laws and distribution of friendship.