Accession Number : AD1046704

Title :   Discovering Social Circles in Ego Networks (Author's Manuscript)

Descriptive Note : Journal Article - Open Access

Corporate Author : Stanford University Stanford United States

Personal Author(s) : McAuley, Julian ; Leskovec,Jure

Full Text :

Report Date : 10 Jan 2013

Pagination or Media Count : 30

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.

Descriptors :   social networks , USER NEEDS

Subject Categories : Radio Communications

Distribution Statement : APPROVED FOR PUBLIC RELEASE