Please use this identifier to cite or link to this item:
Title: Detecting profilable and overlapping communities with user-generated multimedia contents in LBSNs
Authors: Zhao, Y.-L.
Chen, Q.
Yan, S. 
Chua, T.-S. 
Zhang, D.
Keywords: Community detection
Community Profiling
Heterogeneous hypergraph
Location-based social networks
Issue Date: Dec-2013
Citation: Zhao, Y.-L., Chen, Q., Yan, S., Chua, T.-S., Zhang, D. (2013-12). Detecting profilable and overlapping communities with user-generated multimedia contents in LBSNs. ACM Transactions on Multimedia Computing, Communications and Applications 10 (1) : -. ScholarBank@NUS Repository.
Abstract: In location-based social networks (LBSNs), users implicitly interact with each other by visiting places, issuing comments and/or uploading photos. These heterogeneous interactions convey the latent information for identifying meaningful user groups, namely social communities, which exhibit unique location-oriented characteristics. In this work, we aim to detect and profile social communities in LBSNs by representing the heterogeneous interactions with a multimodality nonuniform hypergraph. Here, the vertices of the hypergraph are users, venues, textual comments or photos and the hyperedges characterize the k-partite heterogeneous interactions such as posting certain comments or uploading certain photos while visiting certain places. We then view each detected social community as a dense subgraph within the heterogeneous hypergraph, where the user community is constructed by the vertices and edges in the dense subgraph and the profile of the community is characterized by the vertices related with venues, comments and photos and their inter-relations. We present an efficient algorithm to detect the overlapped dense subgraphs, where the profile of each social community is guaranteed to be available by constraining the minimal number of vertices in each modality. Extensive experiments on Foursquare data well validated the effectiveness of the proposed framework in terms of detecting meaningful social communities and uncovering their underlying profiles in LBSNs. © 2013 ACM.
Source Title: ACM Transactions on Multimedia Computing, Communications and Applications
ISSN: 15516857
DOI: 10.1145/2502415
Appears in Collections:Staff Publications

Show full item record
Files in This Item:
There are no files associated with this item.


checked on May 22, 2020


checked on May 13, 2020

Page view(s)

checked on May 10, 2020

Google ScholarTM



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.