A multi-resolution approach to learning with overlapping communities

Lei Tang, Xufei Wang, Huan Liu, Lei Wang
2010 Proceedings of the First Workshop on Social Media Analytics - SOMA '10  
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
more » ... ctor 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 multiresolution or overlapping property, indicating its highly promising potential in real-world applications ABSTRACT The recent few years have witnessed a rapid surge of participatory 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 multiple different 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 different resolutions and determine which communities are informative of a targeted behavior. We develop an efficient algorithm to extract a hierarchy of overlapping communities. Empirical results on several largescale social media networks demonstrate the superiority of our proposed approach over existing ones without considering the multi-resolution or overlapping property, indicating its highly promising potential in real-world applications.
doi:10.1145/1964858.1964861 dblp:conf/kdd/TangWLW10 fatcat:zjyna7aj4fdyzefbiobt52bouy