Article Information  
Research Community Mining via Generalized Topic Modeling

Keywords: Richer Text Semantics and Relationships, Digital Libraries, Community Mining, Unsupervised Learning

Mehran University Research Journal of Engineering & Technology

Volume 31 ,  Issue 4

Ali   Daud , Muhammad  Akram Shaikh , Faqir Muhammad   ,

References
1. Zaiane, O.R., Chen, J., and Goebel, R.D., “Bconnect: Mining Research Community on DBLP Data”, Joint 9th WEBKDD and 1st SNA-KDD Workshop, San Jose, California, USA, August 12, 2007.
2. Zhang, J., Tang, J., Liang, B., Yang, Z., Wang, S., Zuo, J., and Li, J., “Recommendation Over a Heterogeneous Social Network”, Proceedings of WAIM, China, 2008.
3. Mimno, D., and McCallum, A., “Expertise Modeling for Matching Papers with Reviewers”, Proceedings of the 13th ACM SIGKDD, San Jose, California, pp. 500-509, 2007.
4. Zhang, H., Giles, C.L., Foley, H.C., and Yen, J., “Probabilistic Community Discovery Using Hierarchical Latent Gaussian Mixture Model”, Proceedings of 22nd AAAI Conference on Artificial Intelligence, Vancouver, British Columbia, Canada, pp. 663-668, July 22-26, 2007.
5. Zhou, D., Manavoglu, E., Li, J., Giles, C.L., and Zha, H., “Probabilistic Models for Discovering Ecommunities”, Proceedings of the World Wide Web (WWW), pp. 173-182, 2006.
6. Tang, J., Zhang, J., Yao, L., Li, J., Zhang, L., and Su, Z., “ArnetMiner: Extraction and Mining of Academic Social Networks”, Proceedings of ACM SIGKDD, 2008.
7. Rosen-Zvi, M., Griffiths, T., Steyvers, M., and Smyth, P., “The Author-Topic Model for Authors and Documents”, Proceedings of the 20th UAI, Banff, Canada, 2004.
8. Blei, D.M., Ng, A.Y., and Jordan, M.I., “Latent Dirichlet Allocation”, JMLR, Volume 3, pp. 993-1022, 2003.
9. Andrieu, C., Freitas, N.D., Doucet, A., and Jordan, M., “An Introduction to MCMC for Machine Learning”, Journal of Machine Learning, Volume 50, pp. 5-43, 2003.
10. DBLP Bibliography Database. http://www.informatik.unitrier. de/~ley/db/.
11. Hofmann, T., “Probabilistic Latent Semantic Analysis”, Proceedings of the 15th UAI, Stockholm, Sweden, 1999.
12. Azzopardi, L., Girolami, M., and Risjbergen, K.V., “Investigating the Relationship between Language Model Perplexity and IR Precision-Recall Measures”, Proceedings of the 26th ACM SIGIR, Toronto, Canada, 2003.
13. Tyler, J.R., Wilkinson, D.M., and Huberman, B.A., “Email as Spectroscopy: Automated Discovery of Community Structure within Organizations”, Proceedings of the C & T, pp. 81-96, 2003.
14. Griffiths, T.L., and Steyvers, M., “Finding Scientific Topics”, Proceedings of the NAS, pp. 5228-5235, USA, 2004.
15. Newman, M.E., “Coauthorship Networks and Patterns of Scientific Collaboration”, Proceedings of the National Academy, Volume 1, pp. 5200-5205, USA, 2004.
16. Newman, M.E.J., “Fast Algorithm for Detecting Community Structure in Networks”, Physical Review, Volume 69, pp. 066-133, 2004.
17. Pothen, A., Simon, H., and Liou, K.P., “Partitioning Sparse Matrices with Eigenvectors of Graphs”, SIAM Journal of SIMAX, Volume 11, pp. 430-452, 1990.
18. Palla, G., Derenyi, I., Farkas, I., and Vicsek, T., “Uncovering the Overlapping Community Structure of Complex Networks in Nature and Society”, Nature, pp. 435-814, 2005.
19. Girvan, M., and Newman, M.E.J., “Community Structure in Social and Biological Networks”, Proceedings of the NAS, Volume 99, pp. 8271-8276, USA, 2002.
20. Popescul, A., Flake, G.W., Lawrence, S., Ungar, L.H., and Giles, C.L., “Clustering and Identifying Temporal Trends in Document Databases”, IEEE ADL, pp. 173-182, 2000.
21. Radicchi, F., Castellano, C., Cecconi, F., Loreto, V., and Parisi, D., “Dening and Identifying Communities in Networks”, Proceedings of the NAS, USA, 2004.
22. Ruan, J., and Zhang, W., “Identification and Evaluation of Weak Community Structures in Networks”, Proceedings of the Association for the Advancement of Artificial Intelligence, 2006.
23. Wilkinson, D.M., and Huberman, B.A.A., “Method for Finding Communities of Related Genes”, Proceedings of the National Academy of Volume 1, pp. 5241-5248, USA, 2004.
24. Breese, J., Heckerman, D., and Kadie, C., “Empirical Analysis of Predictive Algorithms for Collaborative Filtering”, Proceedings of the UAI, pp. 43-52, 1998.
25. Deshpande, M,, and Karypis, G., “Item-Based Top-n- Recommendation Algorithms”, ACM Transactions on Information Systems, Volume 22, No. 1, pp. 143-177, 2004.
26. Balabanovic, M., and Shoham, Y., “Content-Based Collaborative Recommendation”, Communications of the ACM, Volume 40, No. 3, 1997.
27. Leskovec, J., Lang, K., Dasgupta, A., and Mahoney, M., “Statistical Properties of Community Structure in Large Social and Information Networks”, Proceedings of the 17th WWW, 2008.
28. Li, H., Nie, Z., Lee, W., Giles, C., and Rong, J., “Scalable Community Discovery on Textual Data with Relations”, Proceedings of the 17th CIKM, 2008.
29. Duan, D., Li, Y., Jin, Y., and Lu, Z., “Community Mining on Dynamic Weighted Directed Graphs”, CNIKM Workshop, November 6, 2009.
30. Lin, Y.R., Sun, J., Castro, P., Konuru, R., Sundaram, H., and Kelliher, A., “MetaFac: Community Discovery via Relational Hypergraph Factorization”, Proceedings of ACM SIGKDD, June 28- July 1, 2009.
31. Yang, T., Jin, R., Chi, Y., and Zhu, S., “Combining Link and Content for Community Detection: A Discriminative Approach”, Proceedings of ACM SIGKDD, June 28- July 1, 2009.
32. Zhou, D., Ji, X., Zha, H., and Giles, C.L., “Topic Evolution and Social Interactions: How Authors Effect Research”, Proceedings of the CIKM, pp. 248-257, 2006.
33. McCallum, A., Nigam, K., and Ungar, L.H., “Efficient Clustering of High-Dimensional Data Sets with Application to Reference Matching”, Proceedings of the 6th ACM SIGKDD, pp. 169-178, 2000.