DBconnect: mining research community on DBLP data

ABSTRACT

Extracting information from large collections of structured, semi-structured or even unstructured data can be a considerable challenge when much of the hidden information is implicit within relationships among entities within the data. Social networks are such data collections in which relationships play a vital role in the knowledge these networks can convey. A bibliographic database is an essential tool for the research community, yet finding and making use of relationships comprised within such a social network is difficult. In this paper we introduce DBconnect, a prototype that exploits the social network coded within the DBLP database by drawing on a new random walk approach to reveal interesting knowledge about the research community and even recommend collaborations.

REFERENCES

• 1: Sergey Brin , Lawrence Page, The anatomy of a large-scale hypertextual Web search engine, Proceedings of the seventh international conference on World Wide Web 7, p.107-117, April 1998, Brisbane, Australia
• 2: DBLP Bibliography database. http://www.informatik.uni-trier.de/~ley/db/.
• 3: AnHai Doan, Raghu Ramakrishnan, Fei Chen, Pedro DeRose, Yoonkyong Lee, Robert McCann, Mayssam Sayyadian, and Warren Shen. Community information management. IEEE Data Engineering Bulletin, Special Issue on Probabilistic Databases, 29(1), 2006.
• 4: Michelle Girvan and M. E. J. Newman. Community structure in social and biological networks. In Proceedings of the National Academy of Science USA, 99:8271-8276, 2002.
• 5: Taher H. Haveliwala, Topic-sensitive PageRank, Proceedings of the 11th international conference on World Wide Web, May 07-11, 2002, Honolulu, Hawaii, USA [doi>10.1145/511446.511513]
• 6: Jingrui He , Mingjing Li , Hong-Jiang Zhang , Hanghang Tong , Changshui Zhang, Manifold-ranking based image retrieval, Proceedings of the 12th annual ACM international conference on Multimedia, October 10-16, 2004, New York, NY, USA [doi>10.1145/1027527.1027531]
• 7: P. Holme, M. Huss, and H. Jeong. Subnetwork hierarchies of biochemical pathways. Bioinformatics, 19:532--538, 2003.
• 8: Glen Jeh , Jennifer Widom, SimRank: a measure of structural-context similarity, Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining, July 23-26, 2002, Edmonton, Alberta, Canada [doi>10.1145/775047.775126]
• 9: George Karypis , Vipin Kumar, Multilevel k-way partitioning scheme for irregular graphs, Journal of Parallel and Distributed Computing, v.48 n.1, p.96-129, Jan. 1998 [doi>10.1006/jpdc.1997.1404]
• 10: B. W. Kernighan and S. Lin. An efficient heuristic procedure for partitioning graphs. Bell System Technical Journal, 49:291--307, 1970.
• 11: Stefan Klink, Patrick Reuther, Alexander Weber, Bernd Walter, and Michael Ley. Analysing social networks within bibliographical data. In DEXA, pages 234--243, 2006.
• 12: Michael Ley, The DBLP Computer Science Bibliography: Evolution, Research Issues, Perspectives, Proceedings of the 9th International Symposium on String Processing and Information Retrieval, p.1-10, September 11-13, 2002
• 13: Sillvio Cesar Cazella and Luis Otavio Campos Alvares. An architecture based on multi-agent system and data mining for recommending research papers and researchers. In Proc. of the 18th International Conference on Software Engineering and Knowledge Engineering (SEKE), pages 67--72, 2006.
• 14: Mario A. Nascimento , Jörg Sander , Jeffrey Pound, Analysis of SIGMOD's co-authorship graph, ACM SIGMOD Record, v.32 n.3, p.8-10, September 2003 [doi>10.1145/945721.945722]
• 15: M. E. J. Newman. The structure and function of complex networks. SIAM Review, 45(2):167--256, 2003.
• 16: Lawrence Page, Sergey Brin, Rajeev Motwani, and Terry Winograd. The pagerank citation ranking: Bringing order to the web. In Technical report, Stanford University Database Group, 1998.
• 17: Jia-Yu Pan , Hyung-Jeong Yang , Christos Faloutsos , Pinar Duygulu, Automatic multimedia cross-modal correlation discovery, Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining, August 22-25, 2004, Seattle, WA, USA [doi>10.1145/1014052.1014135]
• 18: Alex Pothen , Horst D. Simon , Kan-Pu Liou, Partitioning sparse matrices with eigenvectors of graphs, SIAM Journal on Matrix Analysis and Applications, v.11 n.3, p.430-452, Jul. 1990 [doi>10.1137/0611030]
• 19: Filippo Radicchi, Claudio Castellano, Federico Cecconi, Vittorio Loreto, and Domenico Parisi. Defining and identifying communities in networks. PROC.NATL.ACAD.SCI.USA, 101:2658, 2004.
• 20: Alan F. Smeaton , Gary Keogh , Cathal Gurrin , Kieran McDonald , Tom Sødring, Analysis of papers from twenty-five years of SIGIR conferences: what have we been doing for the last quarter of a century?, ACM SIGIR Forum, v.36 n.2, Fall 2002 [doi>10.1145/792550.792556]
• 21: Gilbert Strang. Introduction to linear algebra, Wellesley-Cambridge Press, 3 Edition, 1998.
• 22: Jimeng Sun , Huiming Qu , Deepayan Chakrabarti , Christos Faloutsos, Neighborhood Formation and Anomaly Detection in Bipartite Graphs, Proceedings of the Fifth IEEE International Conference on Data Mining, p.418-425, November 27-30, 2005 [doi>10.1109/ICDM.2005.103]
• 23: Hanghang Tong , Christos Faloutsos , Jia-Yu Pan, Fast Random Walk with Restart and Its Applications, Proceedings of the Sixth International Conference on Data Mining, p.613-622, December 18-22, 2006 [doi>10.1109/ICDM.2006.70]
• 24: Joshua R. Tyler , Dennis M. Wilkinson , Bernardo A. Huberman, Email as spectroscopy: automated discovery of community structure within organizations, Communities and technologies, Kluwer, B.V., Deventer, The Netherlands, 2003
• 25: S. Wasserman and K. Faust. Social network analysis: Methods and applications, Cambridge University Press, 1994.