2009 MetaFacCommunityDiscoveryviaRel

• (Lin et al., 2009) ⇒ Yu-Ru Lin, Jimeng Sun, Paul Castro, Ravi Konuru, Hari Sundaram, and Aisling Kelliher. (2009). “MetaFac: Community Discovery via Relational Hypergraph Factorization.” In: Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2009). doi:10.1145/1557019.1557080

Subject Headings:

Notes

• Categories and Subject Descriptors: H.2.8 Database Management: Database Applications — Data mining; H.3.3 Information Storage and Retrieval: Information Search and Retrieval — Information filtering; I.5.3 Pattern Recognition: Clustering; J.4 Computer Applications: Social and Behavioral Sciences — Economics.
• General Terms: Algorithms, Experimentation, Measurement, Theory.

Cited By

• http://scholar.google.com/scholar?q=%22MetaFac%3A+community+discovery+via+relational+hypergraph+factorization%22+2009
• http://portal.acm.org/citation.cfm?doid=1557019.1557080&preflayout=flat#citedby

Quotes

Author Keywords

MetaFac, Metagraph Factorization, Relational Hypergraph, Nonnegative Tensor Factorization, Community Discovery, Dynamic Social Network Analysis.

Abstract

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 contexts and interactions. Our work has three key contributions : (1) metagraph, a novel relational hypergraph representation for modeling multi-relational and multi-dimensional data; (2) an efficient factorization method for community extraction on a given metagraph; (3) an on-line method to handle time-varying relations through incremental metagraph factorization. Extensive experiments on real-world data collected from the Digg social media website suggest that our technique is scalable and is able to extract meaningful communities based on the social media contexts. We illustrate the usefulness of our framework through prediction tasks. We outperform baseline methods (including aspect model and tensor analysis) by an order of magnitude.

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References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2009 MetaFacCommunityDiscoveryviaRel	Jimeng Sun Yu-Ru Lin Aisling Kelliher Paul Castro Ravi Konuru Hari Sundaram			MetaFac: Community Discovery via Relational Hypergraph Factorization		KDD-2009 Proceedings		10.1145/1557019.1557080		2009