89 Scopus citations

Abstract

With the pervasive availability of Web 2.0 and social networking sites, people can interact with each other easily through various social media. For instance, popular sites like Del.icio.us, Flickr, and YouTube allow users to comment shared content (bookmark, photos, videos), and users can tag their own favorite content. Users can also connect to each other, and subscribe to or become a fan or a follower of others. These diverse individual activities result in a multi-dimensional network among actors, forming cross-dimension group structures with group members sharing certain similarities. It is challenging to effectively integrate the network information of multiple dimensions in order to discover cross-dimension group structures. In this work, we propose a two-phase strategy to identify the hidden structures shared across dimensions in multi-dimensional networks. We extract structural features from each dimension of the network via modularity analysis, and then integrate them all to find out a robust community structure among actors. Experiments on synthetic and real-world data validate the superiority of our strategy, enabling the analysis of collective behavior underneath diverse individual activities in a large scale.

Original languageEnglish (US)
Title of host publicationICDM 2009 - The 9th IEEE International Conference on Data Mining
Pages503-512
Number of pages10
DOIs
StatePublished - Dec 1 2009
Event9th IEEE International Conference on Data Mining, ICDM 2009 - Miami, FL, United States
Duration: Dec 6 2009Dec 9 2009

Publication series

NameProceedings - IEEE International Conference on Data Mining, ICDM
ISSN (Print)1550-4786

Other

Other9th IEEE International Conference on Data Mining, ICDM 2009
CountryUnited States
CityMiami, FL
Period12/6/0912/9/09

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Keywords

  • Community detection
  • Cross-dimension network validation
  • Heterogeneous interaction
  • Heterogeneous network
  • Multi-dimensional networks

ASJC Scopus subject areas

  • Engineering(all)

Cite this

Tang, L., Wang, X., & Liu, H. (2009). Uncovering groups via heterogeneous interaction analysis. In ICDM 2009 - The 9th IEEE International Conference on Data Mining (pp. 503-512). [5360276] (Proceedings - IEEE International Conference on Data Mining, ICDM). https://doi.org/10.1109/ICDM.2009.20