A Recursive Bayesian Approach for the Link Prediction Problem
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Recently, link prediction techniques have been increasingly adopted to discover link patterns in various domains. On challenging problem is to improve the performance continually. In this paper, we propose a recursive prediction mechanism to addresses the link prediction problem. A posterior is calculated based on observed data, and then we estimate the state of the graph and use the posterior as the prior distribution for the next stage. With the increasing of iterations, the proposed approach incorporates more and more topological structure information and node attributes data. Experimental results with real-world networks have shown that the proposed solution performs better in terms of well-known metrics as compared to the existing approaches. This novel approach has already been integrated into an expert system and provides auxiliary support for decision-makers.
Keywords:recursive Bayesian link prediction expert system statistical inference
This work is supported by National Natural Science Foundation of China with grant no. 61572459.
- 1.Wang, D., Pedreschi, C.M., and Song, F., Giannotti and A.L. Barabási, Human mobility, social ties, and link prediction, Knowledge Discovery and Data Mining (KDD), Proceedings of the 17th ACM SIGKDD International Conference On, 2011, pp. 1100–1108.Google Scholar
- 2.Pujari, P. and Kanawati, R., Tag recommendation by link prediction based on supervised machine learning, Weblogs and Social Media (AAAI), Proceedings of the 6th International AAAI Conference On, 2012, pp. 547–550.Google Scholar
- 8.Newman, M.E., Clustering and preferential attachment in growing networks, Phys. Rev., 2001, vol. 64, 025102.Google Scholar
- 15.Yu, Z., Feng, L., Bin, X., Kening, G., and Ge, Y., Using non-topological node attribute to improve results of link prediction in social networks, Proceedings of the 2012 Ninth Web Information Systems and Applications Conference (WISA), 2012.Google Scholar
- 16.Backstrom, L. and Leskovec, J., Supervised random walks: Predicting and recommending links in social networks, Web Search and Data Mining (WSDM), Proceedings of the Fourth ACM International Conference On, 2011, pp. 635–644.Google Scholar
- 17.Soares, P.R.S. and Prudêncio, R.B.C., Time series based link prediction, Neural Networks (IJCNN), The 2012 International Joint Conference On, 2012, pp. 1–7.Google Scholar
- 21.Oravecz, Z., Vandekerckhove, J., and Huentelman, M., Sequential Bayesian updating for big data, in Big Data in Cognitive Science: From Methods to Insights, University of California, 2016.Google Scholar
- 22.John Jay and ARITS Transnational Terrorism Database (JJATT). http://doitapps.jjay.cuny.edu/jjatt. Accessed May 14th, 2017.Google Scholar