Yu-Ru Lin
Aisling Kelliher
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 social contexts and interactions. Our work has three key contributions: (1) metagraph, a novel relational hypergraph representation for modeling multi-relational and multi-dimensional social 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 social 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.
Supplemental Material
- B. BADER and T. KOLDA (2006). Algorithm 862: MATLAB tensor classes for fast algorithm prototyping. TOMS 32(4): 635--653. Google Scholar
Digital Library
- A. BANERJEE, S. BASU and S. MERUGU (2007). Multi-way Clustering on Relation Graphs, SDM, 2007.Google ScholarCross Ref
- R. BEKKERMAN, R. EL-YANIV and A. MCCALLUM (2005). Multi-way distributional clustering via pairwise interactions, ACM Intl. Conf. Proc. Series, 41--48, Google ScholarDigital Library
- D. CHAKRABARTI, R. KUMAR and A. TOMKINS (2006). Evolutionary clustering, SIGKDD, 554--560, Google ScholarDigital Library
- Y. CHI, S. ZHU, Y. GONG and Y. ZHANG (2008). Probabilistic Polyadic Factorization and Its Application to Personalized Recommendation, CIKM, 2008. Google ScholarDigital Library
- N. FRIEDMAN, L. GETOOR, D. KOLLER and A. PFEFFER (1999). Learning probabilistic relational models, IJCAI, 1300--1309, 1999. Google ScholarDigital Library
- K. J-RVELIN and J. KEK-L-INEN (2000). IR evaluation methods for retrieving highly relevant documents, SIGIR, 41--48, 2000. Google ScholarDigital Library
- C. KEMP, J. TENENBAUM, T. GRIFFITHS, T. YAMADA and N. UEDA (2006). Learning Systems of Concepts with an Infinite Relational Model, Proc. of the Natl. Conf. on AI, 381, Google ScholarDigital Library
- R. KUMAR, J. NOVAK and A. TOMKINS (2006). Structure and evolution of online social networks, SIGKDD, 611--617, 2006. Google ScholarDigital Library
- D. LEE and H. SEUNG (2001). Algorithms for non-negative matrix factorization, NIPS, 556--562, 2001.Google Scholar
- Y. LIN, Y. CHI, S. ZHU, H. SUNDARAM and B. TSENG (2008). Facetnet: a framework for analyzing communities and their evolutions in dynamic networks, WWW, 2008. Google ScholarDigital Library
- B. LONG, Z. ZHANG and P. YU (2007). A probabilistic framework for relational clustering, SIGKDD, 470--479, Google ScholarDigital Library
- A. POPESCUL, L. H. UNGAR, D. M. PENNOCK and S. LAWRENCE (2001). Probabilistic Models for Unified Collaborative and Content-Based Recommendation in Sparse-Data Environments, UAI 2001, 437--444, Google ScholarDigital Library
- J. SUN, C. FALOUTSOS, S. PAPADIMITRIOU and P. YU (2007). GraphScope: parameter-free mining of large time-evolving graphs, SIGKDD, 687--696, 2007. Google ScholarDigital Library
- S. ZHU, K. YU, Y. CHI and Y. GONG (2007). Combining content and link for classification using matrix factorization, SIGIR, 487--494, 2007. Google ScholarDigital Library
Index Terms
- MetaFac: community discovery via relational hypergraph factorization
Recommendations
Community Discovery via Metagraph Factorization
This work aims at discovering community structure in rich media social networks through analysis of time-varying, multirelational data. Community structure represents the latent social context of user actions. It has important applications such as ...
Extracting community structure through relational hypergraphs
WWW '09: Proceedings of the 18th international conference on World wide webSocial media websites promote diverse user interaction on media objects as well as user actions with respect to other users. The goal of this work is to discover community structure in rich media social networks, and observe how it evolves over time, ...
Community Discovery from Social Media by Low-Rank Matrix Recovery
Special Sections on Diversity and Discovery in Recommender Systems, Online Advertising and Regular PapersThe pervasive usage and reach of social media have attracted a surge of attention in the multimedia research community. Community discovery from social media has therefore become an important yet challenging issue. However, due to the subjective ...
Comments