A social community based approach for reducing the propagation of infectious diseases in healthcare
ACM SIGMOBILE Mobile Computing and Communications Review
Refining graph partitioning for social network clustering
WISE'10 Proceedings of the 11th international conference on Web information systems engineering
Hybrid clustering of multiple information sources via HOSVD
ISNN'10 Proceedings of the 7th international conference on Advances in Neural Networks - Volume Part II
Community detection via heterogeneous interaction analysis
Data Mining and Knowledge Discovery
Chromatic correlation clustering
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
iTop: interaction based topic centric community discovery on twitter
Proceedings of the 5th Ph.D. workshop on Information and knowledge
Integrating social media data for community detection
MSM'11 Proceedings of the 2011 international conference on Modeling and Mining Ubiquitous Social Media
Detecting profilable and overlapping communities with user-generated multimedia contents in LBSNs
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
Exploiting small world property for network clustering
World Wide Web
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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.