TY - GEN
T1 - Uncovering cross-dimension group structures in multi-dimensional networks
AU - Tang, Lei
AU - Liu, Huan
PY - 2009
Y1 - 2009
N2 - With the proliferation 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 sharing content (bookmark, photos, videos), and users can tag her own favorite content. Users can also connect to friends, and subscribe to or become a fan of other users. These diverse individual activities result in a multi-dimensional network among actors, forming cross-dimension group structures with group members focusing on similar topics. It is challenging to effectively integrate the network information of multiple dimensions to find out the cross-dimension group structure. 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 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.
AB - With the proliferation 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 sharing content (bookmark, photos, videos), and users can tag her own favorite content. Users can also connect to friends, and subscribe to or become a fan of other users. These diverse individual activities result in a multi-dimensional network among actors, forming cross-dimension group structures with group members focusing on similar topics. It is challenging to effectively integrate the network information of multiple dimensions to find out the cross-dimension group structure. 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 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.
UR - http://www.scopus.com/inward/record.url?scp=73449144470&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=73449144470&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:73449144470
SN - 9781615671090
T3 - Society for Industrial and Applied Mathematics - 9th SIAM International Conference on Data Mining 2009, Proceedings in Applied Mathematics
SP - 1374
EP - 1381
BT - Society for Industrial and Applied Mathematics - 9th SIAM International Conference on Data Mining 2009, Proceedings in Applied Mathematics 133
T2 - 9th SIAM International Conference on Data Mining 2009, SDM 2009
Y2 - 30 April 2009 through 2 May 2009
ER -