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Detecting profilable and overlapping communities with user-generated multimedia contents in LBSNs

Published:27 December 2013Publication History
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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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References

  1. 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 ScholarGoogle ScholarDigital LibraryDigital Library
  2. Blei, D. M., Ng, A. Y., and Jordan, M. I. 2003. Latent Dirichlet allocation. J. Mach. Learn. Res. 3, 993--1022. Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. 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 ScholarGoogle Scholar
  4. 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 ScholarGoogle ScholarDigital LibraryDigital Library
  5. 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 ScholarGoogle ScholarDigital LibraryDigital Library
  6. 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 ScholarGoogle ScholarDigital LibraryDigital Library
  7. 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 ScholarGoogle Scholar
  8. Fortunato, S. 2010. Community detection in graphs. Phys. Rep. 486, 3--5, 75--174.Google ScholarGoogle ScholarCross RefCross Ref
  9. 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 ScholarGoogle Scholar
  10. Girvan, M. and Newman, M. E. J. 2002. Community structure in social and biological networks. Proc. Nat. Acad. Sci. 99, 12, 7821.Google ScholarGoogle ScholarCross RefCross Ref
  11. 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 ScholarGoogle Scholar
  12. 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 ScholarGoogle ScholarCross RefCross Ref
  13. 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 ScholarGoogle ScholarDigital LibraryDigital Library
  14. 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 ScholarGoogle ScholarDigital LibraryDigital Library
  15. 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 ScholarGoogle ScholarDigital LibraryDigital Library
  16. 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 ScholarGoogle Scholar
  17. Liu, X. and Murata, T. 2011. Detecting communities in k-partite k-uniform (hyper) networks. J. Comput. Sci. Tech. 26, 5, 778--791. Google ScholarGoogle ScholarDigital LibraryDigital Library
  18. 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 ScholarGoogle ScholarDigital LibraryDigital Library
  19. McCallum, A. K. 2002. Mallet: A machine learning for language toolkit. http://mallet.cs.umass.edu.Google ScholarGoogle Scholar
  20. 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 ScholarGoogle ScholarDigital LibraryDigital Library
  21. 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 ScholarGoogle ScholarCross RefCross Ref
  22. 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 ScholarGoogle Scholar
  23. Newman, M. E. 2006. Finding community structure in networks using the eigenvectors of matrices. Phys. Rev. E 74, 3, 036104.Google ScholarGoogle ScholarCross RefCross Ref
  24. 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 ScholarGoogle ScholarDigital LibraryDigital Library
  25. 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 ScholarGoogle Scholar
  26. 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 ScholarGoogle Scholar
  27. Papadopoulos, S., Kompatsiaris, Y., Vakali, A., and Spyridonos, P. 2012. Community detection in social media. Data Mining Knowl. Dis. 24, 3, 515--554. Google ScholarGoogle ScholarDigital LibraryDigital Library
  28. 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 ScholarGoogle ScholarDigital LibraryDigital Library
  29. 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 ScholarGoogle ScholarDigital LibraryDigital Library
  30. 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 ScholarGoogle ScholarDigital LibraryDigital Library
  31. 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 ScholarGoogle ScholarDigital LibraryDigital Library
  32. 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 ScholarGoogle Scholar
  33. Van, M. E. L. and Zisserman, A. 2010. The pascal visual object classes (voc) challenge. Int. J. Comput. Vis. 88, 3, 3--338. Google ScholarGoogle ScholarDigital LibraryDigital Library
  34. 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 ScholarGoogle ScholarDigital LibraryDigital Library
  35. Vedaldi, A. and Fulkerson, B. 2008. VLFeat: An open and portable library of computer vision algorithms. http://www.vlfeat.org/.Google ScholarGoogle Scholar
  36. 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 ScholarGoogle Scholar
  37. 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 ScholarGoogle ScholarDigital LibraryDigital Library
  38. 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 ScholarGoogle ScholarDigital LibraryDigital Library
  39. Wolfe, A. W. 1997. Social network analysis: Methods and applications. American Ethnol. 24, 1, 219--220.Google ScholarGoogle ScholarCross RefCross Ref
  40. 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 ScholarGoogle Scholar
  41. 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 ScholarGoogle ScholarDigital LibraryDigital Library
  42. 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 ScholarGoogle ScholarDigital LibraryDigital Library
  43. 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 ScholarGoogle ScholarDigital LibraryDigital Library
  44. 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 ScholarGoogle ScholarDigital LibraryDigital Library
  45. 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 ScholarGoogle ScholarDigital LibraryDigital Library
  46. 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 ScholarGoogle Scholar
  47. Zhou, T., Ren, J., Medo, M., and Zhang, Y. C. 2007. Bipartite network projection and personal recommendation. Phy. Rev. E 76, 4, 046115.Google ScholarGoogle Scholar
  48. 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 ScholarGoogle ScholarDigital LibraryDigital Library

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