Toward finding hidden communities based on user profile
- 444 Downloads
We consider the community detection problem from a partially observable network structure where some edges are not observable. Previous community detection methods are often based solely on the observed connectivity relation and the above situation is not explicitly considered. Even when the connectivity relation is partially observable, if some profile data about the vertices in the network is available, it can be exploited as auxiliary or additional information. We propose to utilize a graph structure (called a profile graph) which is constructed via the profile data, and propose a simple model to utilize both the observed connectivity relation and the profile graph. Furthermore, instead of a hierarchical approach, based on the modularity matrix of the network structure, we propose an embedding approach which utilizes the regularization via the profile graph. Various experiments are conducted over two social network datasets and comparison with several state-of-the-art methods is reported. The results are encouraging and indicate that it is promising to pursue this line of research.
KeywordsCommunity discovery Modularity Embedding Regularization
We express sincere gratitude to the reviewers for their careful reading of the manuscript and for providing valuable suggestions to improve the paper.
- Basu, S., Davidson, I., & Wagstaff, K. (Eds.) (2008). Constrained clustering: Advances in algorithms, theory, and applications. Chapman & Hall/CRC Press.Google Scholar
- Chapelle, O., Schölkopf, B., & Zien, A. (Eds.) (2006). Semi-supervised learning. Cambridge: MIT Press.Google Scholar
- Dhillon, I. S. (1997). A new O(n 2 ) algorithm for the symmetric tridiagonal eigenvalue/eigenvector problem. PhD thesis, EECS Department, University of California, Berkeley.Google Scholar
- Mika, P. (2007). Social networks and the semantic web. New York: Springer.Google Scholar
- Ristad, E. (1995). A natural law of succession. Technical Report CS-TR-495-95, Princeton University.Google Scholar
- Watts, D. J. (2004). Six degrees: The science of a connected age. W W Norton & Co Inc.Google Scholar
- Yoshida, T. (2010a). A graph model for clustering based on mutual information. In Proc. PRICAI-2010 (LNAI 6230) (pp. 339–350).Google Scholar
- Yoshida, T. (2010b). A graph model for mutual information based clustering. Journal of Intelligent Information Systems. doi: 10.1007/s10844-010-0132-5.
- Yoshida, T. (2010c). Performance evaluation of constraints in graph-based semi-supervised clustering. In Proc. AMT-2010 (LNAI 6335) (pp. 138–149).Google Scholar