Abstract
This paper presents a new approach for cross community mining and discovery using topic modeling. Our approach identifies automatically the communities in a dataset in an unsupervised way and extracts relationships between these communities. These relationships represent the interaction between communities which helps to identify the cross communities and the shared information between them. Our approach consists of a two layer model based on a statistical framework serving as knowledge discovery tools at different levels of analysis. In the first level communities are discovered using a topic model based method, then cross communities are identified using a statistical measure based on the KL divergence. We empirically demonstrate through extensive experiments using a real social network dataset, that our proposed approach discovers communities and relationships between them.
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Morzy, M.: An analysis of communities in different types of online forums. In: 2010 International Conference on Advances in Social Networks Analysis and Mining (ASONAM), pp. 341–345 (2010)
Zhang, D., Guo, B., Zhiwen, Y.: The emergence of social and community intelligence. IEEE Comput. 44(7), 21–28 (2011)
Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent Dirichlet allocation. J. Mach. Learn. Res. 3, 993–1022 (2003)
Kleinberg, J.M.: Authoritative sources in a hyperlinked environment. J. ACM 46(5), 604–632 (1999)
Kitsuregawa, M., Toyoda, M., Pramudiono, I.: Web community mining and web log mining: commodity cluster based execution. In: Australasian Database Conference (2002)
Flake, G.W., Lawrence, S., Giles, C.L.: Efficient identification of web communities. In: Proceedings of the Sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2000, pp. 150–160. ACM, New York (2000)
Ford, L.R., Fulkerson, D.R.: Flows in Networks. Princeton University Press, Princeton (1962)
Balakrishnan, H., Deo, N.: Discovering communities in complex networks. In: Proceedings of the 44th Annual Southeast Regional Conference, ACM-SE 44, pp. 280–285. ACM, New York (2006)
Zhou, W.-J., Wen, J.-R., Ma, W.-Y., Zhang, H.-J.: A concentric-circle model for community mining in graph structures. Technical report MSR-TR-2002-123, Microsoft Research, Microsoft Corporation One Microsoft Way Redmond, WA 98052, USA (2002)
Bouguessa, M., Dumoulin, B., Wang, S.: Identifying authoritative actors in question-answering forums: the case of yahoo! answers. In: KDD, pp. 866–874 (2008)
Bouguessa, M., Wang, S., Dumoulin, B.: Discovering knowledge-sharing communities in question-answering forums. ACM Trans. Knowl. Discov. Data 5(1), 3:1–3:49 (2010)
Adamic, L.A., Zhang, J., Bakshy, E., Ackerman, M.S.: Knowledge sharing and Yahoo answers: everyone knows something. In: Proceedings of the 17th International Conference on World Wide Web, WWW 2008, pp. 665–674. ACM, New York (2008)
Cai, D., Shao, Z., He, X., Yan, X., Han, J.: Mining hidden community in heterogeneous social networks. In: Proceedings of the 3rd International Workshop on Link Discovery, LinkKDD 2005, pp. 58–65. ACM, New York (2005)
Caldarelli, G.: Communities and clustering in some social networks tutorial. In: Proceedings of the International Workshop and Conference on Network Science (NetSci) (2007)
Zhang, H., Giles, C.L., Foley, H.C., Yen, J.: Probabilistic community discovery using hierarchical latent Gaussian mixture model. In: AAAI, pp. 663–668 (2007)
Zhang, H., Qiu, B., Giles, C.L., Foley, H.C., Yen, J.: An LDA-based community structure discovery approach for large-scale social networks. In: ISI, pp, 200–207 (2007)
Henderson, K., Eliassi-Rad, T., Papadimitriou, S., Faloutsos, C.: HCDF: a hybrid community discovery framework. In: SDM, pp. 754–765 (2010)
Wang, X., McCallum, A.: Topics over time: a non-Markov continuous-time model of topical trends. In: Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2006), pp. 424–433 (2006)
Griffiths, T.L., Steyvers, M.: Finding scientific topics. Proc. Natl. Acad. Sci. 101(Suppl. 1), 5228–5235 (2004)
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Chikhaoui, B., Tshimula, J.M., Wang, S. (2021). Community Mining and Cross-Community Discovery in Online Social Networks. In: Barolli, L., Li, K., Enokido, T., Takizawa, M. (eds) Advances in Networked-Based Information Systems. NBiS 2020. Advances in Intelligent Systems and Computing, vol 1264. Springer, Cham. https://doi.org/10.1007/978-3-030-57811-4_17
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DOI: https://doi.org/10.1007/978-3-030-57811-4_17
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