Jian Huang
ABSTRACT
A formal type of scientific and academic collaboration is coauthorship which can be represented by a coauthorship network. Coauthorship networks are among some of the largest social networks and offer us the opportunity to study the mechanisms underlying large-scale real world networks. We construct such a network for the Computer Science field covering research collaborations from 1980 to 2005, based on a large dataset of 451,305 papers authored by 283,174 distinct researchers. By mining this network, we first present a comprehensive study of the network statistical properties for a longitudinal network at the overall network level as well as for the intermediate community level. Major observations are that the database community is the best connected while the AI community is the most assortative, and that the Computer Science field as a whole shows a collaboration pattern more similar to Mathematics than to Biology. Moreover, the small world phenomenon and the scale-free degree distribution accompany the growth of the network. To study the individual collaborations, we propose a novel stochastic model, Stochastic Poisson model with Optimization Tree (Spot)to efficiently predict any increment of collaboration based on the local neighborhood structure. Spot models the non-stationary Poisson process by maximizing the log-likelihood with a tree structure. Empirical results show that Spot outperforms Support Vector Regression by better fitting collaboration records and predicting the rate of collaboration
- R. Albert and A. Barabási. Statistical mechanics of complex networks. Cond-mat/0106096v1 2001.Google Scholar
- L. Amaral, A. Barthelemy, and H. Stanley. Classes of small-world networks. Proceedings of the National Academy of Sciences 97:11149--11152, 2000.Google ScholarCross Ref
- A. Barabási and R. Albert. Emergence of scaling in random networks. Science 286:509--512, 1999.Google ScholarCross Ref
- A. Barabási, H. Jeong, E. Ravasz, Z. Neda, A. Schubert, and T. Vicsek. Evolution of the social network of scientific collaborations. Cond-mat/0104162 2001.Google Scholar
- C. Borgs, J. Chayes, M. Mahdian, and A. Saberi. Exploring the community structure of newsgroups. In Proc. of 10th ACM SIGKDD Conf. Knowledge Discovery and Data Mining 2004. Google ScholarDigital Library
- L. Breiman, J. Friedman, R. Olshen, and C. Stone. Classification and Regression Trees Wadsworth, 1984.Google Scholar
- C. Chang and C. Lin. LIBSVM: a library for support vector machines 2001.Google Scholar
- P. Doreian and F. N. Stokman, editors. Evolution of Social Networks Gordon and Breach, New York, 1997.Google Scholar
- E. Elmacioglu and D. Lee. On six degrees of separation in DBLP-DB and more. ACM SIGMOD Record 34:33--40, 2005. Google ScholarDigital Library
- H. Han, C. L. Giles, E. Manavoglu, H. Zha, Z. Zhang, and E. A. Fox. Automatic document metadata extraction using support vector machines. In ACM/IEEE Joint Conference on Digital Libraries (JCDL)2003. Google ScholarDigital Library
- J. Huang, S. Ertekin, and C. L. Giles. Efficient name disambiguation for large scale databases. In Proceedings of the 10th European Conference on Principles and Practice of Knowledge Discovery in Databases (PKDD)2006. Google ScholarDigital Library
- G. Kossinets and D. J. Watts. Empirical analysis of an evolving social network. Science 331:88--90, 2006.Google ScholarCross Ref
- R. Kumar, J. Novak, and A. Tomkins. Structure and evolution of online social networks. In Proc. of the 12th ACM International Conf. on Knowledge Discovery and Data Mining (KDD)pages 611--617, 2006. Google ScholarDigital Library
- J. Leskovec, J. Kleinberg, and C. Faloutsos. Graphs over time: Densification laws, shrinking diameters and possible explanations.In Proceedings of 11th ACM SIGKDD Conf. Knowledge Discovery and Data Mining pages 177--187, 2005. Google ScholarDigital Library
- D. Liben-Nowell and J. Kleinberg. The link-prediction problem for social networks.Journal of the American Society for Information Science and Technology 58(7):1019--1031, 2007. Google ScholarDigital Library
- L. Licamele and L. Getoor. Social capital in friendship-event networks. In Proceedings of Sixth IEEE International Conference on Data Mining (ICDM)pages 959--964, 2006. Google ScholarDigital Library
- F. Liljeros, C. Edling, and L. Amaral. Sexual networks: implications for the transmission of sexually transmitted infections. Microbes and Infection 5:189--196, 2003.Google ScholarCross Ref
- S. Milgram. The small-world problem. Psychology Today 1:61--67, 1967.Google Scholar
- M. E. J. Newman. Who is the best connected scientist? a study of scientific coauthorship networks. Physical Review E 64:016132, 2001.Google ScholarCross Ref
- M. E. J. Newman. Clustering and preferential attachment in growing networks. Physical Review Letters E, 64(025102)2001.Google Scholar
- M. E. J. Newman. Scientific collaboration networks: I. network construction and fundamental results. Physical Revivew E 64, 2001.Google Scholar
- M. E. J. Newman. Scientific collaboration networks: II. shortest paths, weighted networks, and centrality. Physical Revivew E 64, 2001.Google Scholar
- M. E. J. Newman. The structure of scientific collaboration networks.Proceedings of the National Academy of Sciences 98:404--409, 2001.Google ScholarCross Ref
- M. E. J. Newman. Mixing patterns in networks. Physical Review E 67:026126, 2003.Google ScholarCross Ref
- M. E. J. Newman. Coauthorship networks and patterns of scientific collaboration. Proceedings of the National Academy of Sciences 101:5200--5205, 2004.Google ScholarCross Ref
- M. E. J. Newman and J. Park. Why social networks are different from other types of networks. Physical Review E 68:036122, 2003.Google ScholarCross Ref
- S. Redner. Citation statistics from more than a century of physical review. APS Meeting Abstracts 2004.Google Scholar
- S. M. Ross. Introduction to Probability Models Academic Press, 2006. Google ScholarDigital Library
- J. Ruan and W. Zhang. Identification and evaluation of weak community structures in networks. In Proceedings of National Conference on Artificial Intelligence (AAAI)2006. Google ScholarDigital Library
- V. Vapnik. The Nature of Statistical Learning Theory Springer-Verlag, 1999. Google ScholarDigital Library
Index Terms
- Collaboration over time: characterizing and modeling network evolution
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