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A multi-resolution approach to learning with overlapping communities

Conference Paper


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Abstract


  • The recent few years have witnessed a rapid surge of par-

    ticipatory web and social media, enabling a new laboratory

    for studying human relations and collective behavior on an

    unprecedented scale. In this work, we attempt to harness

    the predictive power of social connections to determine the

    preferences or behaviors of individuals such as whether a

    user supports a certain political view, whether one likes one

    product, whether he/she would like to vote for a presidential

    candidate, etc. Since an actor is likely to participate in mul-

    tiple dierent communities with each regulating the actor's

    behavior in varying degrees, and a natural hierarchy might

    exist between these communities, we propose to zoom into

    a network at multiple dierent resolutions and determine

    which communities are informative of a targeted behavior.

    We develop an ecient algorithm to extract a hierarchy of

    overlapping communities. Empirical results on several large-

    scale social media networks demonstrate the superiority of

    our proposed approach over existing ones without consider-

    ing the multi-resolution or overlapping property, indicating

    its highly promising potential in real-world applications

Authors


  •   Tang, Lei (external author)
  •   Wang, Xufei (external author)
  •   Liu, Huan (external author)
  •   Wang, Lei

Publication Date


  • 2010

Citation


  • Tang, L., Wang, X., Liu, H. & Wang, L. (2010). A multi-resolution approach to learning with overlapping communities. International Workshop on Social Media Analytics, in conjunction with the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 1-9).

Scopus Eid


  • 2-s2.0-79956023386

Ro Full-text Url


  • http://ro.uow.edu.au/cgi/viewcontent.cgi?article=1540&context=eispapers

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers/534

Has Global Citation Frequency


Start Page


  • 1

End Page


  • 9

Abstract


  • The recent few years have witnessed a rapid surge of par-

    ticipatory web and social media, enabling a new laboratory

    for studying human relations and collective behavior on an

    unprecedented scale. In this work, we attempt to harness

    the predictive power of social connections to determine the

    preferences or behaviors of individuals such as whether a

    user supports a certain political view, whether one likes one

    product, whether he/she would like to vote for a presidential

    candidate, etc. Since an actor is likely to participate in mul-

    tiple dierent communities with each regulating the actor's

    behavior in varying degrees, and a natural hierarchy might

    exist between these communities, we propose to zoom into

    a network at multiple dierent resolutions and determine

    which communities are informative of a targeted behavior.

    We develop an ecient algorithm to extract a hierarchy of

    overlapping communities. Empirical results on several large-

    scale social media networks demonstrate the superiority of

    our proposed approach over existing ones without consider-

    ing the multi-resolution or overlapping property, indicating

    its highly promising potential in real-world applications

Authors


  •   Tang, Lei (external author)
  •   Wang, Xufei (external author)
  •   Liu, Huan (external author)
  •   Wang, Lei

Publication Date


  • 2010

Citation


  • Tang, L., Wang, X., Liu, H. & Wang, L. (2010). A multi-resolution approach to learning with overlapping communities. International Workshop on Social Media Analytics, in conjunction with the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 1-9).

Scopus Eid


  • 2-s2.0-79956023386

Ro Full-text Url


  • http://ro.uow.edu.au/cgi/viewcontent.cgi?article=1540&context=eispapers

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers/534

Has Global Citation Frequency


Start Page


  • 1

End Page


  • 9