Co-clustering documents and words using bipartite spectral graph partitioning
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
The Journal of Machine Learning Research
Finding community structure in mega-scale social networks: [extended abstract]
Proceedings of the 16th international conference on World Wide Web
Analysis of a Location-Based Social Network
CSE '09 Proceedings of the 2009 International Conference on Computational Science and Engineering - Volume 04
Graph clustering based on structural/attribute similarities
Proceedings of the VLDB Endowment
Distance matters: geo-social metrics for online social networks
WOSN'10 Proceedings of the 3rd conference on Online social networks
Discovering Overlapping Groups in Social Media
ICDM '10 Proceedings of the 2010 IEEE International Conference on Data Mining
Proceedings of the 19th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
Tips, dones and todos: uncovering user profiles in foursquare
Proceedings of the fifth ACM international conference on Web search and data mining
Active tracking in mobile networks: An in-depth view
Computer Networks: The International Journal of Computer and Telecommunications Networking
Cross-domain community detection in heterogeneous social networks
Personal and Ubiquitous Computing
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With the recent surge of location-based social networks (LBSNs, e.g., Foursquare, Facebook Places), huge amount of digital footprints about users' locations, profiles as well as their online social connections become accessible to service providers. Different from social networks (e.g., Flickr, Facebook) which have explicit groups for users to subscribe or join, LBSNs usually have no explicit community structure. In order to capitalize on the large number of potential users, quality community detection approach is needed so as to enable applications such as direct marketing, group tracking, etc. The diversity of people's interests and behaviors when using LBSNs suggests that their community structures overlap. In this paper, based on the user-venue check-in relationship and user/venue attributes, we come out with a novel multi-mode multi-attribute edge-centric co-clustering (M2Clustering) framework to discover the overlapping communities of LBSNs users. By employing inter-mode/intra-mode features, the proposed framework is able to group like-minded users from different social perspectives. The efficacy of our approach is validated by intensive empirical evaluations using the collected Foursquare dataset of 266,838 users with 9,803,764 check-ins over 2,477,122 venues worldwide.