Abstract
The scientific breakthroughs resulting from the collaborations between researchers often outperform the expectations. But finding the partners who will bring this synergic effect can take time and sometime gets nowhere considering the huge amounts of experts in various disciplines. We propose to build a link predictor in a network where nodes represent researchers and links - coauthorships. In this method we use the structure of the constructed graph, and propose to add a semantic and event based approach to improve the accuracy of the predictor. In this case, predictors might offer good suggestions for future collaborations. We will be able to compute the classification of a massive dataset in a reasonable time by under-sampling and balancing the data. This model could be extended in other fields where the research of partnership is important as in world of institutions, associations or companies. We believe that it could also help with finding communities of topics, since link predictors contain implicit information about the semantic relation between researchers.
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References
Adamic, L.A., Adar, E.: Friends and neighbors on the web. Social Networks 25(3), 211–230 (2003)
Brin, S., Page, L.: The anatomy of a large-scale hypertextual Web search engine. Computer Networks and ISDN Systems 30(1–7), 107–117 (1998)
Chawla, N.V.: Data mining for imbalanced datasets: An overview. In: The Data Mining and Knowledge Discovery Handbook, pp. 853–867. Springer, Heidelberg (2005)
Chawla, N.V., Bowyer, K.W., Hall, L.O., Kegelmeyer, W.P.: Smote: Synthetic minority over-sampling technique. Journal of Artificial Intelligence and Research 16, 321–357 (2002)
The DBLP computer science bibliography (2008), http://dblp.uni-trier.de/xml/
Getoor, L., Diehl, C.P.: Link mining: a survey. SIGKDD Explor. Newsl. 7(2), 3–12 (2005)
Hasan, M.A.: Link prediction using supervised learning. In: Proceedings of the Workshop on Link Analysis, Counter-terrorism and Security (2006)
Jeh, G., Widom, J.: Simrank: a measure of structural-context similarity. In: Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 538–543. ACM Press, New York (2002)
Kashima, H., Abe, N.: A parameterized probabilistic model of network evolution for supervised link prediction. In: Proceedings of the Sixth International Conference on Data Mining, pp. 340–349. IEEE Computer Society Press, Los Alamitos (2006)
Katz, L.: A new status index derived from sociometric analysis. Psychometrika 18(1), 39–43 (1953)
Liben-Nowell, D., Kleinberg, J.: The link prediction problem for social networks. In: Proceedings of the twelfth international conference on Information and knowledge management, pp. 556–559. ACM Press, New York (2003)
Manning, C.D., Raghavan, P., Schütze, H.: Introduction to Information Retrieval. Cambridge University Press, Cambridge (2008)
Newman, M.E.J.: Clustering and preferential attachment in growing networks. Physical Review EÂ 64, 025102 (2001)
Newman, M.E.J.: The structure and function of complex networks. SIAM Review 45(2), 167–256 (2003)
Pavlov, M., Ichise, R.: Finding experts by link prediction in co-authorship networks. In: Proceedings of the 2nd International Workshop on Finding Experts on the Web with Semantics (November 2007)
Popescul, A., Ungar, L.H.: Statistical relational learning for link prediction. In: Proceedings of Workshop on Learning Statistical Models from Relational Data (2003)
Ross Quinlan, J.: C4.5: Programs for Machine Learning. Morgan Kaufmann, San Francisco (1993)
Taskar, B., Wong, M.-F., Abbeel, P., Koller, D.: Link prediction in relational data. In: Proceedings of Neural Information Processing Systems (2003)
Witten, I.H., Frank, E.: Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations. Morgan Kaufmann, San Francisco (1999)
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Wohlfarth, T., Ichise, R. (2008). Semantic and Event-Based Approach for Link Prediction. In: Yamaguchi, T. (eds) Practical Aspects of Knowledge Management. PAKM 2008. Lecture Notes in Computer Science(), vol 5345. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-89447-6_7
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DOI: https://doi.org/10.1007/978-3-540-89447-6_7
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