ABSTRACT
The identification of communities in social networks is a common problem that researchers have been dealing using network analysis properties. However, in environments where community members are connected by digital documents, most researchers have either emphasize to solve the community discovery problem computing structural properties of networks, ignoring the underlying semantic information from digital documents. In this paper, we propose a novel approach to combine traditional network analysis methods for community detection with text mining techniques. This way, extracted communities can be labeled according to latent semantic information within documents, called topics. Our proposal was evaluated in Plexilandia, a virtual community of practice with more than 2,500 members and 9 years of commentaries.
- Rakesh Agrawal, Johannes Gehrke, Dimitrios Gunopulos, and Prabhakar Raghavan. Automatic subspace clustering of high dimensional data for data mining applications. SIGMOD Rec., 27:94-105, 1998. Google ScholarDigital Library
- David M. Blei, Andrew Y. Ng, and Michael I. Jordan. Latent dirichlet allocation. J. Mach. Learn. Res., 3:993-1022, 2003. Google ScholarDigital Library
- Vincent D. Blondel, Jean-Loup Guillaume, Renaud Lambiotte, and Etienne Lefebvre. Fast unfolding of communities in large networks. Journal of Statistical Mechanics: Theory and Experiment, (10):1-12, 2008.Google Scholar
- Aaron Clauset, M. E. J. Newman, and Cristopher Moore. Finding community structure in very large networks. Phys. Rev. E, 70(6):066111, Dec 2004.Google Scholar
- Santo Fortunato. Community detection in graphs. Physics Reports, 486(3-5):75-174, 2010.Google Scholar
- Gaston L'Huillier, Hector Alvarez, Sebastián A. Ríos, and Felipe Aguilera. Topic-based social network analysis for virtual communities of interests in the dark web. SIGKDD Explor. Newsl., 12:66-73, 2011. Google ScholarDigital Library
- Andrew McCallum, Xuerui Wang, and Andrés Corrada-Emmanuel. Topic and role discovery in social networks with experiments on enron and academic email. J. Artif. Int. Res., 30:249-272, 2007. Google ScholarDigital Library
- M. E. J. Newman and M. Girvan. Finding and evaluating community structure in networks. Phys. Rev. E, 69(2):026113, Feb 2004.Google Scholar
- Nishith Pathak, Colin DeLong, Arindam Banerjee, and Kendrick Erickson. Social topic models for community extraction. In SNA-KDD '08: 2nd ACM Workshop on Social Network Mining and Analysis, Las Vegas, Nevada, USA, 2008.Google Scholar
- Sebastián A. Ríos, Felipe Aguilera, and Luis A. Guerrero. Virtual communities of practice's purpose evolution analysis using a concept-based mining approach. In Proceedings of the 13th International Conference on Knowledge-Based and Intelligent Information and Engineering Systems: Part II, KES '09, pages 480-489, Berlin, Heidelberg, 2009. Springer-Verlag. Google ScholarDigital Library
- Gerard Salton, Anita Wong, and Chung-Shu Yang. A vector space model for automatic indexing. Commun. ACM, Vol. 18(11):613-620, 1975. Google ScholarDigital Library
- Stanley Wasserman and Katherine Faust. Social network analysis: methods and applications. Cambridge University Press, 1994.Google ScholarCross Ref
- Duncan J. Watts and Steven H. Strogatz. Collective dynamics of 'small-world' networks. Nature, 393(6684):440-442, 1998.Google Scholar
- Etienne Wenger, Richard Arnold McDermott, and William Snyder. Cultivating communities of practice: a guide to managing knowledge. Harvard Business Press, 2002. Google ScholarDigital Library
Index Terms
- Enhancing Community Discovery and Characterization in VCoP Using Topic Models
Recommendations
Latent Community Topic Analysis: Integration of Community Discovery with Topic Modeling
This article studies the problem of latent community topic analysis in text-associated graphs. With the development of social media, a lot of user-generated content is available with user networks. Along with rich information in networks, user graphs ...
Dark Web portal overlapping community detection based on topic models
ISI-KDD '12: Proceedings of the ACM SIGKDD Workshop on Intelligence and Security InformaticsA hot research topic is the study and monitoring of online communities. Of course, homeland security institutions from many countries are using data mining techniques to perform this task, aiming to anticipate and avoid a possible menace to local peace. ...
Topic-based social network analysis for virtual communities of interests in the Dark Web
ISI-KDD '10: ACM SIGKDD Workshop on Intelligence and Security InformaticsThe study of extremist groups and their interaction is a crucial task in order to maintain homeland security and peace. Tools such as social networks analysis and text mining have contributed to the understanding of this kind of groups in order to ...
Comments