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.
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- Amitay, E., Carmel, D., Har'El, N., Ofek-Koifman, S., Soffer, A., Yogev, S., and Golbandi, N. 2009. Social search and discovery using a unified approach. In Proceedings of the Conference on Hypertext and Hypermedia. ACM, 199--208. Google Scholar
Digital Library
- Blei, D. M., Ng, A. Y., and Jordan, M. I. 2003. Latent Dirichlet allocation. J. Mach. Learn. Res. 3, 993--1022. Google Scholar
Digital Library
- Cao, L., Qi, G. J., Tsai, S. F., Tsai, M. H., Pozo, A. D., Huang, T. S., Zhang, X., and Lim, S. H. 2011. Multimedia information networks in social media. Social Netw. Data Anal. 413--445.Google Scholar
- Crandall, D. J., Backstrom, L., Huttenlocher, D., and Kleinberg, J. 2009. Mapping the world's photos. In Proceedings of the International Conference on World Wide Web. ACM, 761--770. Google Scholar
Digital Library
- Dhillon, I. S., Guan, Y., and Kulis, B. 2007. Weighted graph cuts without eigenvectors a multilevel approach. IEEE Trans. Patt. Anal. Mach. Intell. 29, 11, 1944--1957. Google Scholar
Digital Library
- El-Arini, K., Paquet, U., Herbrich, R., Van Gael, J., and Agüera y Arcas, B. 2012. Transparent user models for personalization. In Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD'12). ACM, New York, 678--686. Google Scholar
Digital Library
- Fang, Q., Sang, J., Xu, C., and Lu, K. 2013. Paint the city colorfully: Location visualization from multiple themes. In Advances in Multimedia Modeling. Lecture Notes in Computer Science, vol. 7732, Springer Berlin, 92--105.Google Scholar
- Fortunato, S. 2010. Community detection in graphs. Phys. Rep. 486, 3--5, 75--174.Google Scholar
Cross Ref
- Gill, A. J., Nowson, S., and Oberlander, J. 2009. What are they blogging about? personality, topic and motivation in blogs. In Proceedings of the 3rd International AAAI Conference on Weblogs and Social Media.Google Scholar
- Girvan, M. and Newman, M. E. J. 2002. Community structure in social and biological networks. Proc. Nat. Acad. Sci. 99, 12, 7821.Google Scholar
Cross Ref
- Goswami, S., Sarkar, S., and Rustagi, M. 2009. Stylometric analysis of bloggers? Age and gender. In Proceedings of the 3rd International AAAI Conference on Weblogs and Social Media.Google Scholar
- Guimera, R., Sales-Pardo, M., and Amaral, L. A. N. 2004. Modularity from fluctuations in random graphs and complex networks. Phys. Rev. E 70, 2, 025101.Google Scholar
Cross Ref
- Leskovec, J., Lang, K. J., and Mahoney, M. 2010. Empirical comparison of algorithms for network community detection. In Proceedings of the 19th International Conference on World Wide Web (WWW'10). ACM, New York, 631--640. Google Scholar
Digital Library
- Li, N. and Chen, G. 2009. Analysis of a location-based social network. In Proceedings of the International Conference on Computational Science and Engineering (CSE'09). Vol. 4, 263--270. Google Scholar
Digital Library
- Lin, Y.-R., Sundaram, H., De Choudhury, M., and Kelliher, A. 2012. Discovering multirelational structure in social media streams. ACM Trans. Multimedia Comput. Commun. Appl. 8, 1, 4. Google Scholar
Digital Library
- Liu, H., Latecki, L. J., and Yan, S. 2010. Robust clustering as ensembles of affinity relations. Proceedings of Advances in Neural Information Processing Systems.Google Scholar
- Liu, X. and Murata, T. 2011. Detecting communities in k-partite k-uniform (hyper) networks. J. Comput. Sci. Tech. 26, 5, 778--791. Google Scholar
Digital Library
- Lu, C., Hu, X., and ran Park, J. 2011. Exploiting the social tagging network for web clustering. IEEE Trans. Syst. Man Cybernet. Part A: Systems and Humans, 41, 5, 840--852. Google Scholar
Digital Library
- McCallum, A. K. 2002. Mallet: A machine learning for language toolkit. http://mallet.cs.umass.edu.Google Scholar
- Mei, T., Li, L., Hua, X.-S., and Li, S. 2012. Imagesense: Towards contextual image advertising. ACM Trans. Multimedia Comput. Commun. Appl. 8, 1, Article 6. Google Scholar
Digital Library
- Murata, T. and Ikeya, T. 2010. A new modularity for detecting one-to-many correspondence of communities in bipartite networks. Adv. Compl. Syst. 13, 1, 19--31.Google Scholar
Cross Ref
- Neubauer, N. and Obermayer, K. 2009. Towards community detection in k-partite k-uniform hypergraphs. In Proceedings of the NIPS Workshop on Analyzing Networks and Learning with Graphs.Google Scholar
- Newman, M. E. 2006. Finding community structure in networks using the eigenvectors of matrices. Phys. Rev. E 74, 3, 036104.Google Scholar
Cross Ref
- Nie, L., Wang, M., Zha, Z., Li, G., and Chua, T.-S. 2011. Multimedia answering: Enriching text qa with media information. In Proceedings of the 34th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR'11). ACM, New York, 695--704. Google Scholar
Digital Library
- Noulas, A., Scellato, S., Mascolo, C., and Pontil, M. 2011a. An empirical study of geographic user activity patterns in foursquare. In Proceedings of the 5th International AAAI Conference on Weblogs and Social Media.Google Scholar
- Noulas, A., Scellato, S., Mascolo, C., and Pontil, M. 2011b. Exploiting semantic annotations for clustering geographic areas and users in location-based social networks. In Proceedings of the Workshop Social Mobile Web.Google Scholar
- Papadopoulos, S., Kompatsiaris, Y., Vakali, A., and Spyridonos, P. 2012. Community detection in social media. Data Mining Knowl. Dis. 24, 3, 515--554. Google Scholar
Digital Library
- Perronnin, F., Sánchez, J., and Mensink, T. 2010. Improving the Fisher kernel for large-scale image classification. In Proceedings of the 11th European Conference on Computer Vision: Part IV (ECCV'10). Springer-Verlag, Berlin, 143--156. Google Scholar
Digital Library
- Scellato, S., Noulas, A., and Mascolo, C. 2011. Exploiting place features in link prediction on location-based social networks. In Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD'11). ACM, New York, 1046--1054. Google Scholar
Digital Library
- Tang, L. and Liu, H. 2009. Scalable learning of collective behavior based on sparse social dimensions. In Proceedings of the 18th ACM Conference on Information and Knowledge Management (CIKM'09). ACM, New York, 1107--1116. Google Scholar
Digital Library
- Tang, L., Wang, X., and Liu, H. 2009. Uncoverning groups via heterogeneous interaction analysis. In Proceedings of the 2009 9th IEEE International Conference on Data Mining (ICDM'09). IEEE Computer Society, 503--512. Google Scholar
Digital Library
- Tang, L., Wang, X., and Liu, H. 2010. Understanding emerging social structures: A group profiling approach. School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tech. rep. TR-10-002.Google Scholar
- Van, M. E. L. and Zisserman, A. 2010. The pascal visual object classes (voc) challenge. Int. J. Comput. Vis. 88, 3, 3--338. Google Scholar
Digital Library
- Vasconcelos, M. A., Ricci, S., Almeida, J., Benevenuto, F., and Almeida, V. 2012. Tips, dones and todos: uncovering user profiles in foursquare. In Proceedings of the 5th ACM International Conference on Web Search and Data Mining (WSDM'12). ACM, New York, 653--662. Google Scholar
Digital Library
- Vedaldi, A. and Fulkerson, B. 2008. VLFeat: An open and portable library of computer vision algorithms. http://www.vlfeat.org/.Google Scholar
- Vedaldi, A., Chatfield, K., Lempitsky, V., and Zisserman, A. 2011. The devil is in the details: An evaluation of recent feature encoding methods. In Proceedings of the British Machine Vision Conference. J. Hoey, S. McKenna, and E. Trucco, Eds., BMVA Press, 76.1--76.12.Google Scholar
- Wang, M., Ni, B., Hua, X.-S., and Chua, T.-S. 2012. Assistive tagging: A survey of multimedia tagging with human-computer joint exploration. ACM Comput. Surv. 44, 4, Article 25. Google Scholar
Digital Library
- Wang, X., Tang, L., Gao, H., and Liu, H. 2010. Discovering overlapping groups in social media. In Proceedings of the 2010 IEEE International Conference on Data Mining (ICDM'10). IEEE Computer Society, 569--578. Google Scholar
Digital Library
- Wolfe, A. W. 1997. Social network analysis: Methods and applications. American Ethnol. 24, 1, 219--220.Google Scholar
Cross Ref
- Xiao, J., Hays, J., Ehinger, K. A., Oliva, A., and Torralba, A. 2010. Sun database: Large-scale scene recognition from abbey to zoo. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 3485--3492. DOI: http://dx.doi.org/10.1109/CVPR.2010.5539970.Google Scholar
- Xie, J., Kelley, S., and Szymanski, B. K. 2011. Overlapping community detection in networks: The State of the art and comparative study. Arxiv preprint arXiv:1110.5813. Google Scholar
Digital Library
- Xu, B., Bu, J., Chen, C., and Cai, D. 2012. An exploration of improving collaborative recommender systems via user-item subgroups. In Proceedings of the International Conference on World Wide Web. ACM, 21--30. Google Scholar
Digital Library
- Yang, Y., Yang, Y., Huang, Z., Shen, H. T., and Nie, F. 2011. Tag localization with spatial correlations and joint group sparsity. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR'11). IEEE Computer Society. 881--888. Google Scholar
Digital Library
- Zhao, Y.-L., Zheng, Y.-T., Zhou, X., and Chua, T.-S. 2011. Generating representative views of landmarks via scenic theme detection. In Proceedings of the International Conference on Advances in Multimedia Modeling - Volume Part I. Springer-Verlag, 392--402. Google Scholar
Digital Library
- Zheng, V. W., Zheng, Y., Xie, X., and Yang, Q. 2010. Collaborative location and activity recommendations with gps history data. In Proceedings of the 19th International Conference on World Wide Web (WWW'10). ACM, New York, 1029--1038. Google Scholar
Digital Library
- Zheng, Y.-T., Zhao, M., Song, Y., Adam, H., Buddemeier, U., Bissacco, A., Brucher, F., Chua, T.-S., and Neven, H. 2009. Tour the world: Building a web-scale landmark recognition engine. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR'09). 1085--1092.Google Scholar
- Zhou, T., Ren, J., Medo, M., and Zhang, Y. C. 2007. Bipartite network projection and personal recommendation. Phy. Rev. E 76, 4, 046115.Google Scholar
- Zhuang, J., Mei, T., Hoi, S. C. H., Xu, Y.-Q., and Li, S. 2011. When recommendation meets mobile: Contextual and personalized recommendation on the go. In Proceedings of the 13th International Conference on Ubiquitous Computing (UbiComp'11). ACM, New York, 153--162. Google Scholar
Digital Library
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Detecting profilable and overlapping communities with user-generated multimedia contents in LBSNs
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