Group Profiling for Understanding Social Structures
ACM Transactions on Intelligent Systems and Technology (TIST)
Infectious communities forging: using information diffusion model in social network mining
WISM'11 Proceedings of the 2011 international conference on Web information systems and mining - Volume Part II
Detecting overlapping communities in folksonomies
Proceedings of the 23rd ACM conference on Hypertext and social media
Unsupervised feature selection for linked social media data
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Content-based crowd retrieval on the real-time web
Proceedings of the 21st ACM international conference on Information and knowledge management
Predicting aggregate social activities using continuous-time stochastic process
Proceedings of the 21st ACM international conference on Information and knowledge management
Integrating social media data for community detection
MSM'11 Proceedings of the 2011 international conference on Modeling and Mining Ubiquitous Social Media
Detecting overlapping communities in location-based social networks
SocInfo'12 Proceedings of the 4th international conference on Social Informatics
Identifying Overlying Group of People through Clustering
International Journal of Information Technology and Web Engineering
Identifying same wavelength groups from twitter: a sentiment based approach
ACIIDS'13 Proceedings of the 5th Asian conference on Intelligent Information and Database Systems - Volume Part II
Network denoising in social media
Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
Detecting profilable and overlapping communities with user-generated multimedia contents in LBSNs
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
Cross-domain community detection in heterogeneous social networks
Personal and Ubiquitous Computing
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The increasing popularity of social media is shortening the distance between people. Social activities, e.g., tagging in Flickr, book marking in Delicious, twittering in Twitter, etc. are reshaping people’s social life and redefining their social roles. People with shared interests tend to form their groups in social media, and users within the same community likely exhibit similar social behavior (e.g., going for the same movies, having similar political viewpoints), which in turn reinforces the community structure. The multiple interactions in social activities entail that the community structures are often overlapping, i.e., one person is involved in several communities. We propose a novel co-clustering framework, which takes advantage of networking information between users and tags in social media, to discover these overlapping communities. In our method, users are connected via tags and tags are connected to users. This explicit representation of users and tags is useful for understanding group evolution by looking at who is interested in what. The efficacy of our method is supported by empirical evaluation in both synthetic and online social networking data.