Abstract
This paper focuses on the problem of predicting the topic of a paper based on the co-authorship graph. Co-authorship graph is an undirected graph in which paper is represented by a node and two nodes are linked together by a link if they share at least one common author. The approach of link-based object classification (LBC) is based on the assumption that papers in the same neighbourhoods of the co-authorship graph tend to have same topic, and the predicted topic for one node in the graph depends on the topics of the another nodes that linked to it. In order to solve LBC, we have a traditional relaxation labeling to be proposed by Hoche, S., and Flach. Based on this algorithm, we propose an improvement of this algorithm. Our proposed algorithm has the processing speed faster than the traditional one. We test the performance of the proposed algorithm with the ILPnet2 database and compare the experimental result with the traditional algorithm.
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References
Getoor, L., Friedman, N., Koller, D., Taskar, B.: Learning probabilistic models of link structure. Journal of Machine Learning Research 3, 679–707 (2003)
Hoche, S., Flach, P.: Predicting Topics of Scientific Papers from Co-Authorship Graphs: a Case Study. In: Proceedings of the 2006 UK Workshop on Computational Intelligence (UKCI 2006), pp. 215–222 (September 2006)
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© 2013 Springer-Verlag Berlin Heidelberg
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Hoang, N.T., Do, P., Le, H.N. (2013). A Fast Algorithm for Predicting Topics of Scientific Papers Based on Co-authorship Graph Model. In: Nguyen, N., Trawiński, B., Katarzyniak, R., Jo, GS. (eds) Advanced Methods for Computational Collective Intelligence. Studies in Computational Intelligence, vol 457. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34300-1_8
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DOI: https://doi.org/10.1007/978-3-642-34300-1_8
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-34299-8
Online ISBN: 978-3-642-34300-1
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