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Folksonomy-based ad hoc community detection in online social networks

Social Network Analysis and Mining volume 2, pages 305–328 (2012)Cite this article

Abstract

Advances in Web 2.0 technologies have led to unstructured information overload in the complex and multidimensional datasets that originate in social networks. This overload can diminish the quality of web-related services such as recommender systems in the social web and can overwhelm users with irrelevant information. Such overload indicates the need to design and develop efficient and accurate information management and retrieval systems in social networks and anticipate recommendations in the semantic context of user interest. Community detection in social networks can help identify higher-order structures that unveil insight into networks and their functional organization and reduce irrelevant information received by the user. In this study, the application of community detection in online social networks is investigated within the framework of topic discovery-based user link identification and is consequently used to uncover implicit user communities (ad hoc communities). In the proposed approach, we use a graph-based information extraction technique that provides for a personalized information retrieval (recommender) system. We hypothesize that the ad hoc communities of users sharing similar interests embedded in a folksonomy-based social network can be identified by overlapping tag clusters in the tag concept hierarchy. Our methodology incorporates the novel information extraction techniques of topic modeling for topic extraction (feature extraction), user profile modeling for user profile extraction, and community extraction from the social graph, modeled in a framework to derive relevant ad hoc user communities of social interest from folksonomy data. Our experimental results demonstrate an accuracy of 70–98 % in community detection using data obtained from CiteULike®.

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Notes

  1. http://www.facebook.com/.

  2. http://twitter.com/.

  3. http://www.wikipedia.org/.

  4. http://www.flickr.com/.

  5. http://technorati.com/.

  6. http://www.yahoo.com/.

  7. http://www.youtube.com/.

  8. https://delicious.com/.

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Acknowledgments

This material is based on work supported in part by Air Force Office of Sponsored Research (AFOSR) under Award No. FA9550-10-1-0289. Any opinions, findings, and conclusions or recommendations expressed in this publication are those of the author(s) and do not necessarily reflect the views of the AFOSR.

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  1. Computer Science Program, Louisiana Tech University, Ruston, LA, 71270, USA

    Vasanth Nair & Sumeet Dua

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  1. Vasanth Nair
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  2. Sumeet Dua
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Correspondence to Sumeet Dua.

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Nair, V., Dua, S. Folksonomy-based ad hoc community detection in online social networks. Soc. Netw. Anal. Min. 2, 305–328 (2012). https://doi.org/10.1007/s13278-012-0081-9

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  • DOI: https://doi.org/10.1007/s13278-012-0081-9

Keywords

  • Social networks
  • Community detection
  • Recommender systems
  • Information retrieval
  • Feature extraction
  • Information extraction