Abstract
With the increase in size of online social network, the need to predict the future links among the nodes is enlarged. In this paper, an efficient prediction of links in the online social network is performed by considering link weights along with the temporal information. Although the existing technique is based on either weighted networks or time series based, the link prediction is based on the combination of weighted network and temporal data; and then applying supervised and unsupervised learning algorithm to predict the future link among the nodes (users) in the online social networking sites. Our task is to investigate that a weighted temporal network can be used with supervised learning to achieve a high-performance link prediction. Here research focus is to take weighted as well as unweighted network and to apply a similarity function for generating a set of connected nodes, then a time series is built for every pair of nonconnected nodes, and forecasting model is deployed on the time series. The final results obtained using supervised and unsupervised learning shown acceptable results when a weighted temporal network is used.
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Wasserman, S., & Faust, K. (1994). Social network analysis. In Methods and applications. Cambridge University press.
Wang, C., & Satuluri, V. (2007). Local probabilistic models for link prediction. In ICDM 2007.
Hasan, M. A., Chaoji, V., Salem, S., & Zaki, M. (2006). Link prediction using supervised learning. In Proceedings of SDM 06 workshop on Link Analysis, Counterterrorism and Security.
Ricardo, P., Soares, D.S., Ricardo, B.C.P. (2012). Time series based link prediction. In WCCI2012 IEEE World Congress on Computional Intelligence.
Xiang, E. W. (2008). A survey on link prediction models for social network data. Science And Technology.
Getoor, L., & Diehl, C. P. (2005). Link mining: A survey. SigKDD Explorations Special Issue on Link Mining.
Liben-Nowell, D., & Kleinberg, J. (2007). The link-prediction problem for social networks. Journal of the American Society for Information Science and Technology, 58, 1019–1031.
Murata, T., & Moriyasu, S. (2008). Link prediction based on structural properties of online social networks. New Generation Computing, 26(3), 245–257.
Huang, Z. (2006). Link prediction based on graph topology: The predictive value of the generalized clustering coefficient.
Barabasi, A. L., & Bonabeau, E. (2003). Scale-free networks. Scientific American, 288(5), 60–69.
Berlingerio, M., Bonchi, F., Bringmann, B., & Gionis, A. (2009). Mining graph evolution rules. In Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part I, ECML PKDD’09 (pp 115–130). Springer.
Tan, P.-N., Steinbach, M., & Kumar, V. (2005). Introduction to data mining. Addison Wesley.
Newman, M. E. J. (2001). Clustering and preferential attachment in growing networks, Physical Review Letters E, 64.
Potgieter, A., April, K. A., Cooke, R. J. E., & Osunmakinde, I. O. (2007). Temporality in link prediction: Understanding social complexity.
Huang, Z., & Dennis, K. J. L. (2009). The time-series link prediction problem with applications in communication surveillance. INFORMS Journal on Computing, 21, 286–303.
Rodrigues, H., & Ricardo, B.C P. (2011). Supervised link prediction in weighted networks. In Proceedings of International Joint Conference on Neural Networks, IEEE.
Wheelwright, S.C., & Makridakis, S.G. (1985). Forecasting methods for management. In Systems and controls for financial management series. Wiley.
Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., & Witten, I. H. (2009). The weka data mining software: An update. SIGKDD Explorations, 11(1), 10–18.
Pfahringer, B., Holmes, G., & Kirkby, R. (2001). Optimizing the induction of alternating decision trees. In Proceedings of the Fifth Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining (pp. 477–487).
Quinlan, J. R. (1986). Induction of decision trees. Mach. Learn., 1(1), 81–106.
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Gupta, A., Sharma, S., Shivhare, H. (2016). Supervised Link Prediction Using Forecasting Models on Weighted Online Social Network. In: Satapathy, S., Joshi, A., Modi, N., Pathak, N. (eds) Proceedings of International Conference on ICT for Sustainable Development. Advances in Intelligent Systems and Computing, vol 409. Springer, Singapore. https://doi.org/10.1007/978-981-10-0135-2_24
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DOI: https://doi.org/10.1007/978-981-10-0135-2_24
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