MetaFac: community discovery via relational hypergraph factorization

This paper aims at discovering community structure in rich media social networks, through analysis of time-varying, multi-relational data. Community structure represents the latent social context of user actions. It has important applications in information tasks such as search and recommendation. Social media has several unique challenges. (a) In social media, the context of user actions is constantly changing and co-evolving; hence the social context contains time-evolving multi-dimensional relations. (b) The social context is determined by the available system features and is unique in each social media website. In this paper we propose MetaFac (MetaGraph Factorization), a framework that extracts community structures from various social contexts and interactions. Our work has three key contributions: (1) metagraph, a novel relational hypergraph representation for modeling multirelational and multi-dimensional social data; (2) an efficient factorization method for community extracti...

• Type: Conference
• Year: 2009
• Where: KDD
• Authors: Yu-Ru Lin, Jimeng Sun, Paul Castro, Ravi B. Konuru, Hari Sundaram, Aisling Kelliher
• Keywords: KDD 2009, Social, Social Contexts, Social Media