@inproceedings{lin_extracting_2009,
  title = {Extracting {{Community Structure}} through {{Relational Hypergraphs}}},
  booktitle = {Proceedings of the 18th {{International Conference}} on {{World Wide Web}} ({{WWW}} 2009)},
  author = {Lin, Yu-Ru and Sun, Jimeng and Castro, Paul and Konuru, Ravi and Sundaram, Hari and Kelliher, Aisling},
  year = {2009},
  pages = {1213--1214},
  publisher = ACM,
  address = {{New York, NY, USA}},
  doi = {10.1145/1526709.1526934},
  url = {http://doi.acm.org/10.1145/1526709.1526934},
  urldate = {2013-08-26},
  abstract = {Social 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, through analysis of multi-relational data. The problem is important in the enterprise domain where extracting emergent community structure on enterprise social media, can help in forming new collaborative teams, aid in expertise discovery, and guide long term enterprise reorganization. Our approach consists of three main parts: (1) a relational hypergraph model for modeling various social context and interactions; (2) a novel hypergraph factorization method for community extraction on multi-relational social data; (3) an on-line method to handle temporal evolution through incremental hypergraph factorization. Extensive experiments on real-world enterprise data suggest that our technique is scalable and can extract meaningful communities. To evaluate the quality of our mining results, we use our method to predict users' future interests. Our prediction outperforms baseline methods (frequency counts, pLSA) by 36-250\% on the average, indicating the utility of leveraging multi-relational social context by using our method.},
  file = {/Users/yuru/Dropbox/zotero/storage/XHNZBWIV/Lin et al. - 2009 - Extracting community structure through relational .pdf},
  isbn = {978-1-60558-487-4},
  keywords = {community evolution,dynamic social network analysis,non-negative tensor factorization,relational hypergraph},
  lccn = {10},
  note = {poster},
  series = {{{WWW}} '09}
}

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The goal of this work is to discover community structure in rich media social networks, and observe how it evolves over time, through analysis of multi-relational data. The problem is important in the enterprise domain where extracting emergent community structure on enterprise social media, can help in forming new collaborative teams, aid in expertise discovery, and guide long term enterprise reorganization. Our approach consists of three main parts: (1) a relational hypergraph model for modeling various social context and interactions; (2) a novel hypergraph factorization method for community extraction on multi-relational social data; (3) an on-line method to handle temporal evolution through incremental hypergraph factorization. Extensive experiments on real-world enterprise data suggest that our technique is scalable and can extract meaningful communities. To evaluate the quality of our mining results, we use our method to predict users' future interests. 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