Discovering Social Circles in Ego Networks

Abstract

People's personal social networks are big and cluttered, and currently there is no good way to automatically organize them. Social networking sites allow users to manually categorize their friends into social circles (e.g. 'circles' on Google+, and 'lists' on Facebook and Twitter), however they are laborious to construct and must be updated whenever a user's network grows. In this paper, we study the novel task of automatically identifying users' social circles. We pose this task as a multi-membership node clustering problem on a user's ego-network, a network of connections between her friends. We develop a model for detecting circles that combines network structure as well as user profile information. For each circle we learn its members and the circle-specific user profile similarity metric. Modeling node membership to multiple circles allows us to detect overlapping as well as hierarchically nested circles. Experiments show that our model accurately identifies circles on a diverse set of data from Facebook, Google+, and Twitter, for all of which we obtain hand-labeled ground-truth.

30 pages, 10 figures, extended version of our NIPS 2012 paper

References in corpus (6)

• Modularity and community structure in networks
• Uncovering the overlapping community structure of complex networks in nature and society
• Detecting the overlapping and hierarchical community structure of complex networks
• Benchmarks for testing community detection algorithms on directed and weighted graphs with overlapping communities
• Community Detection as an Inference Problem
• Empirical Comparison of Algorithms for Network Community Detection