ABSTRACT
Collaborative research has been increasingly popular and important in academic circles. However, there is no open platform available for scholars or scientists to effectively discover potential collaborators. This paper discusses CollabSeer, an open system to recommend potential research collaborators for scholars and scientists. CollabSeer discovers collaborators based on the structure of the coauthor network and a user's research interests. Currently, three different network structure analysis methods that use vertex similarity are supported in CollabSeer: Jaccard similarity, cosine similarity, and our relation strength similarity measure. Users can also request a recommendation by selecting a topic of interest. The topic of interest list is determined by CollabSeer's lexical analysis module, which analyzes the key phrases of previous publications. The CollabSeer system is highly modularized making it easy to add or replace the network analysis module or users' topic of interest analysis module. CollabSeer integrates the results of the two modules to recommend collaborators to users. Initial experimental results over a subset of the CiteSeerX database show that CollabSeer can efficiently discover prospective collaborators.
- R. Albert and A. Barabasi. Statistical mechanics of complex networks. Reviews of Modern Physics, 74(1):47--97, 2002.Google ScholarCross Ref
- A. Barabasi and R. Albert. Emergence of scaling in random networks. Science, 286(5439):509, 1999.Google ScholarCross Ref
- A. Barabasi, R. Albert, and H. Jeong. Scale-free characteristics of random networks: the topology of the world-wide web. Physica A: Statistical Mechanics and its Applications, 281(1--4):69--77, 2000.Google Scholar
- A. Barabasi and Z. Oltvai. Network biology: understanding the cell's functional organization. Nature Reviews Genetics, 5(2):101--113, 2004.Google ScholarCross Ref
- S. Boccalettia, V. Latorab, Y. Morenod, M. Chavezf, and D. Hwanga. Complex networks: structure and dynamics. Physics Reports, 424:175--308, 2006.Google ScholarCross Ref
- A. Cucchiarelli and F. D'Antonio. Mining Potential Partnership through Opportunity Discovery in Research Networks. In Advances in Social Networks Analysis and Mining (ASONAM), 2010 International Conference on, pages 404--406. IEEE, 2010. Google ScholarDigital Library
- C. Desrosiers and G. Karypis. Enhancing link-based similarity through the use of non-numerical labels and prior information. In Proceedings of the Eighth Workshop on Mining and Learning with Graphs, pages 26--33. ACM, 2010. Google ScholarDigital Library
- S. Dorogovtsev and J. Mendes. Evolution of networks. Advances in Physics, 51(4):1079--1187, 2002.Google ScholarCross Ref
- M. Gastner and M. Newman. The spatial structure of networks. The European Physical Journal B-Condensed Matter and Complex Systems, 49(2):247--252, 2006.Google ScholarCross Ref
- M. Girvan and M. Newman. Community structure in social and biological networks. Proceedings of the National Academy of Sciences of the United States of America, 99(12):7821, 2002.Google ScholarCross Ref
- L. Gou, H. Chen, J. Kim, X. Zhang, and C. Giles. Sndocrank: a social network-based video search ranking framework. In Proceedings of the International Conference on Multimedia Information Retrieval, pages 367--376. ACM, 2010. Google ScholarDigital Library
- L. Gou, X. Zhang, H. Chen, J. Kim, and C. Giles. Social network document ranking. In Proceedings of the 10th Annual Joint Conference on Digital Libraries, pages 313--322. ACM, 2010. Google ScholarDigital Library
- L. Hamers, Y. Hemeryck, G. Herweyers, M. Janssen, H. Keters, R. Rousseau, and A. Vanhoutte. Similarity measures in scientometric research: the jaccard index versus salton's cosine formula. Information Processing & Management, 25(3):315--318, 1989. Google ScholarDigital Library
- Q. He, J. Pei, D. Kifer, P. Mitra, and L. Giles. Context-aware citation recommendation. In Proceedings of the 19th International Conference on World Wide Web, pages 421--430. ACM, 2010. Google ScholarDigital Library
- J. Huang, Z. Zhuang, J. Li, and C. Giles. Collaboration over time: characterizing and modeling network evolution. In Proceedings of the International Conference on Web Search and Web Data Mining, pages 107--116. ACM, 2008. Google ScholarDigital Library
- G. Jeh and J. Widom. Simrank: A measure of structural-context similarity. In Proceedings of the Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pages 538--543. ACM, 2002. Google ScholarDigital Library
- J. Katz and B. Martin. What is research collaboration? Research Policy, 26(1):1--18, 1997.Google ScholarCross Ref
- M. Kendall. A new measure of rank correlation. Biometrika, 30(1--2):81, 1938.Google Scholar
- J. Kleinberg. Authoritative sources in a hyperlinked environment. Journal of the ACM (JACM), 46(5):604--632, 1999. Google ScholarDigital Library
- R. Kumar, J. Novak, and A. Tomkins. Structure and evolution of online social networks. Link Mining: Models, Algorithms, and Applications, pages 337--357, 2010.Google Scholar
- E. Leicht, P. Holme, and M. Newman. Vertex similarity in networks. Physical Review E, 73(2):26120, 2006.Google ScholarCross Ref
- C. Li, J. Han, G. He, X. Jin, Y. Sun, Y. Yu, and T. Wu. Fast computation of simrank for static and dynamic information networks. In Proceedings of the 13th International Conference on Extending Database Technology, pages 465--476. ACM, 2010. Google ScholarDigital Library
- A. Lotka and W. A. of Sciences. The frequency distribution of scientific productivity. Washington Academy of Sciences, 1926.Google Scholar
- A. Mislove, M. Marcon, K. Gummadi, P. Druschel, and B. Bhattacharjee. Measurement and analysis of online social networks. In Proceedings of the 7th ACM SIGCOMM Conference on Internet Measurement, pages 29--42. ACM, 2007. Google ScholarDigital Library
- M. Newman. The structure of scientific collaboration networks. Proceedings of the National Academy of Sciences of the United States of America, 98(2):404, 2001.Google ScholarCross Ref
- M. Newman. The structure and function of complex networks. SIAM REVIEW, 45:167--256, 2003.Google ScholarDigital Library
- M. Newman. Coauthorship networks and patterns of scientific collaboration. Proceedings of the National Academy of Sciences of the United States of America, 101(Suppl 1):5200, 2004.Google ScholarCross Ref
- E. Otte and R. Rousseau. Social network analysis: a powerful strategy, also for the information sciences. Journal of Information Science, 28(6):441, 2002.Google ScholarCross Ref
- E. Ravasz, A. Somera, D. Mongru, Z. Oltvai, and A. Barabasi. Hierarchical organization of modularity in metabolic networks. Science, 297(5586):1551, 2002.Google ScholarCross Ref
- G. Salton. Automatic text processing: the transformation, analysis, and retrieval of information by computer. 1989. Google ScholarDigital Library
- B. Sarwar, G. Karypis, J. Konstan, and J. Reidl. Item-based collaborative filtering recommendation algorithms. In Proceedings of the 10th International Conference on World Wide Web, pages 285--295. ACM, 2001. Google ScholarDigital Library
- K. Sugiyama and M. Kan. Scholarly paper recommendation via user's recent research interests. In Proceedings of the 10th Annual Joint Conference on Digital libraries, pages 29--38. ACM, 2010. Google ScholarDigital Library
- P. Tan, M. Steinbach, V. Kumar, et al. Introduction to data mining. Pearson Addison Wesley Boston, 2006. Google ScholarDigital Library
- P. Treeratpituk and C. L. Giles. Disambiguating authors in academic publications using random forests. In Proceedings of the 9th ACM/IEEE-CS Joint Conference on Digital Libraries, pages 39--48. ACM, 2009. Google ScholarDigital Library
- D. Watts and S. Strogatz. Collective dynamics of small-world networks. Nature, 393(6684):440--442, 1998.Google ScholarCross Ref
- I. Witten, G. Paynter, E. Frank, C. Gutwin, and C. Nevill-Manning. Kea: Practical automatic keyphrase extraction. In Proceedings of the Fourth ACM Conference on Digital Libraries, pages 254--255. ACM, 1999. Google ScholarDigital Library
- T. Wohlfarth and R. Ichise. Semantic and event-based approach for link prediction. Practical Aspects of Knowledge Management, pages 50--61, 2008. Google ScholarDigital Library
- P. Zhao, J. Han, and Y. Sun. P-rank: a comprehensive structural similarity measure over information networks. In Proceeding of the 18th ACM Conference on Information and Knowledge Management, pages 553--562. ACM, 2009. Google ScholarDigital Library
- T. Zhou, L. L¨ u, and Y.-C. Zhang. Predicting missing links via local information. The European Physical Journal B-Condensed Matter and Complex Systems, 71(4):623--630, 2009.Google ScholarCross Ref
Index Terms
- CollabSeer: a search engine for collaboration discovery
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