Abstract
The widespread popularity and vigorous growth of micro-blogging systems provides a fertile source for analyzing social networks and phenomenon. Currently, few data mining tools can deal with unique characteristics of microblogging systems. In this study, we propose an integrate approach for mining user relationships in micro-blogging systems. The approach starts from macroscopic analysis of social networks by grouping users with the method of maximal strongly connected components (MSCC). Following that, a measure of condensation level of groups are calculated to find out the most influential group , and all groups can be ranked according to this measure; then a new algorithm is presented to evaluate the influence of a specific user within a group. The integrated approach is capable to analyze large amount data sets. It is useful for exploring directions of information diffusion and evaluating the scope and the strength of individual user’s influence in micro-blogging systems.
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Java, A., Song, X., Finin, T., Tseng, B.: Why We Twitter: Understanding Microblogging Usage and Communities. In: The Joint 9th WEBKDD and 1st SNA-KDD Workshop 2007, pp. 56–65. ACM, New York (2007)
Chin, A., Chignell, M.: A Social Hypertext Model for Finding Community in Blogs. In: 17th Conference on Hypertext and Hypermedia, pp. 11–22. ACM, New York (2006)
Newman, M.E.J., Girvan, M.: Finding and Evaluating Community Structure in Networks. J. Phys. Rev. 69(2), 26113 (2004)
Girvan, M.: Community Structure in Social and Biological Networks. PNAS 99(12), 7821–7826 (2002)
Dijkstra, E.W.: A Note on Two Problems in Connexion with Graphs. J. Num. Math. 1(1), 269–271 (1959)
Ahn, Y., Han, S., Kwak, H., Moon, S., Jeong, H.: Analysis of topological Characteristics of Huge Online Social Networking Services. In: 16th International Conference on World Wide Web, pp. 835–844. ACM, New York (2007)
Hsu, W.H., Lancaster, J., Paradesi, M.S.R., Weninger, T.: Structural Link Analysis from User Profiles and Friends Networks: A Feature Construction Approach. In: ICWSM 2007, pp. 75–80. ACM, New York (2007)
Golder, S., Wilkinson, D., Huberman, B.: Rhythms of Social Interaction: Messaging within a Massive Online Network. J. Com. and Tech. 2007, 41–66 (2007)
Tyler, J., Wilkinson, D., Huberman, B.: E-Mail as Spectroscopy: Automated Discovery of Community Structure within Organizations. J. Info. Soc. 21(2), 43–53 (2005)
McCallum, A., Wang, X., Corrada-Emmanuel, A.: Topic and Role Discovery in Social Networks with Experiments on Enron and Academic Email. J. Arti. Inte. Res. 30, 249–272 (2007)
Matsumura, N., Goldberg, D., Llorà , X.: Mining Directed Social Network from Message Board. In: 14th International Conference on World Wide Web, pp. 1092–1093. ACM, New York (2005)
Matsumura, N.: Topic Diffusion in a Community. In: Ohsawa, Y., McBurney, P. (eds.) Chance Discovery, pp. 84–97. Springer, Heidelberg (2003)
Kazienko, P., Musiał, K.: Mining Personal Social Features in the Community of Email Users. In: Geffert, V., Karhumäki, J., Bertoni, A., Preneel, B., Návrat, P., Bieliková, M. (eds.) SOFSEM 2008. LNCS, vol. 4910, pp. 708–719. Springer, Heidelberg (2008)
Han, J., Kamber, M.: Data Mining: Concepts and Techniques, 2nd edn. Morgan Kaufmann, San Francisco (2006)
Samudrala, R., Moult, J.: A Graph-theoretic Algorithm for Comparative Modeling of Protein Structure. J. Mol. Biol. 279(1), 287–302 (1998)
Cai, D., Shao, Z., He, X.F., Yan, X.F., Han, J.W.: Mining Hidden Community in Heterogeneous Social Networks. In: The 3rd International Workshop on Link Discovery, pp. 1–26. ACM, New York (2005)
Garton, L., Haythorntwaite, C., Wellman, B.: Studying Online Social Networks. JCMC 3(1), http://www.ascusc.org/jcmc/vol3/issue1/garton.html
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Gao, Q., Qu, Q., Zhang, X. (2011). Mining Social Relationships in Micro-blogging Systems. In: Ozok, A.A., Zaphiris, P. (eds) Online Communities and Social Computing. OCSC 2011. Lecture Notes in Computer Science, vol 6778. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21796-8_12
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DOI: https://doi.org/10.1007/978-3-642-21796-8_12
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