Abstract
Many researchers have studied about complex networks such as the World Wide Web, social networks and the protein interaction network. One hot topic in this area is community detection. For example, in the WWW, the community shows a set of web pages about a certain topic. The community structure is unquestionably a key characteristic of complex networks. We have proposed the novel community extracting method. The method considers the overlaps between communities using the idea of the intersection graph. Additionally, we address the problem of edge inhomogeneity by weiting edges using content information. Finally, we conduct clustering based on modularity. In this paper, we evaluate our method through applying to real microblog networks.
Chapter PDF
Similar content being viewed by others
Keywords
References
Albert, R., Barabasi, A.-L.: Statistical mechanics of complex networks. Rev. Mod. Phys. 74, 47–97 (2002)
Boccaletti, S., Latora, V., Moreno, Y., Chavez, M., Hwang, D.U.: Complex Networks: Structure and Dynamics. Phys. Rep. 424(4–5), 175–308 (2006)
Everett, M.G., Borgatti, S.P.: Analyzing Clique Overlap. Connections 21(1), 49–61 (1998)
Flake, G.W., Lawrence, S., Giles, C.L., Coetzee, F.: Self-Organization of the Web and Identification of Communities. IEEE Computer 35(3), 66–71 (2002)
Hung, B.Q., Otsubo, M., Hijikata, Y., Nishida, S.: HITS Algorithm Improvement using Semantic Text Portion. WIAS 8(2), 149–164 (2010)
Huss, M., Holme, P.: Currency and commodity metabolites: Their identification and relation to the modularity of metabolic networks. IET Systems Biology 1(5), 280–285 (2006)
Jiang, J.J., Conrath, D.W.: Semantic Similarity Based on Corpus Statistics and Lexical Taxonomy. In: Proc. ROCLING 1997, pp. 19–33 (1997)
Kuramochi, T., Okada, N., Tanikawa, K., Hijikata, Y., Nishida, S.: Community Extracting Using Intersection Graph and Content Analysis in Complex Network. In: Proc. WI 2012, pp. 222–229 (2012)
Manning, C.D., Schütze, H.: Foundations of statistical natural language processing. MIT Press (2002)
McKee, T.A., McMorris, F.R.: Topics in Intersection Graph Theory, vol. 2. SIAM, Discrete Mathematics and Applications (1999)
Newman, M.E.J.: Detecting community structure in networks. Eur. Phys. J. B 38(2), 321–330 (2004)
Newman, M.E.J., Girvan, M.: Finding and evaluating community structure in networks. Phys. Rev. EÂ 69(2) (2004)
Newman, M.E.J.: Fast algorithm for detecting community structure in networks. Phys. Rev. EÂ 69(6) (2004)
Newman, M.E.J.: Finding community structure in networks using the eigenvectors of matrices. Phys. Rev. EÂ 74 (2006)
Palla, G., Derenyi, I., Farkas, I., Vicsek, T.: Uncovering the overlapping community structure of complex networks in nature and society. Nature 435(7043), 814–818 (2005)
Rasmussen, E.: Clustering Algorithms, Information Retrieval: Data Structures and Algorithms. In: Frakes, W.B., Baeza-Yates, R. (eds.), pp. 419–442. Prentice-Hall (1992)
Reichardt, J., Bornholdt, S.: Statistical mechanics of community detection. Phys. Rev. EÂ 74(1), 16110 (2006)
Salton, G., Wong, A., Yang, C.S.: A vector space model for automatic indexing. Communications ACM 18(11), 613–620 (1975)
Scott, J.: Social Network Analysis: A Handbook, 2nd edn. Sage Publications (2000)
Scripps, J., Tan, P.-N., Esfahanian, A.-H.: Node Roles and Community Structure in Networks. In: WebKDD/SNA-KDD 2007, pp.26–35 (2007)
Tasgin, M., Bingol, H.: Community Detection in Complex Networks using Genetic Algorithm. In: Proc. ECCS (2007)
Wasserman, S., Faust, K.: Social Network Analysis: Methods and Applications. Cambridge University Press (1994)
Zhang, S., Wang, R., Zhang, X.: Identification of overlapping community structure in complex networks using fuzzy c-means clustering. Physica A 374(1), 483–490 (2007)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Kuramochi, T., Okada, N., Tanikawa, K., Hijikata, Y., Nishida, S. (2013). Applying to Twitter Networks of a Community Extraction Method Using Intersection Graph and Semantic Analysis. In: Kurosu, M. (eds) Human-Computer Interaction. Users and Contexts of Use. HCI 2013. Lecture Notes in Computer Science, vol 8006. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39265-8_35
Download citation
DOI: https://doi.org/10.1007/978-3-642-39265-8_35
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-39264-1
Online ISBN: 978-3-642-39265-8
eBook Packages: Computer ScienceComputer Science (R0)