Co-clustering documents and words using bipartite spectral graph partitioning
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
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
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
Event-based social networks: linking the online and offline social worlds
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
The importance of being placefriends: discovering location-focused online communities
Proceedings of the 2012 ACM workshop on Workshop on online social networks
Hybrid SN: Interlinking Opportunistic and Online Communities to Augment Information Dissemination
UIC-ATC '12 Proceedings of the 2012 9th International Conference on Ubiquitous Intelligence and Computing and 9th International Conference on Autonomic and Trusted Computing
Investigating City Characteristics Based on Community Profiling in LBSNs
CGC '12 Proceedings of the 2012 Second International Conference on Cloud and Green Computing
Detecting overlapping communities in location-based social networks
SocInfo'12 Proceedings of the 4th international conference on Social Informatics
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With the recent surge of location-based social networks (LBSNs), e.g., Foursquare and Facebook Places, huge amount of human digital footprints that people leave in the cyber-physical space become accessible, including users' profiles, online social connections, and especially the places that they have checked in. 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. Meanwhile, unlike social networks which only contain a single type of social interaction, the coexistence of online/offline social interactions and user/venue attributes in LBSNs makes the community detection problem much more challenging. In order to capitalize on the large number of potential users/venues as well as the huge amount of heterogeneous social interactions, quality community detection approach is needed. In this paper, by exploring the heterogenous digital footprints of LBSNs users in the cyber-physical space, we come out with a novel edge-centric co-clustering framework to discover overlapping communities. By employing inter-mode as well as 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 based on the collected Foursquare dataset.