Abstract
With the fast growing of Web 2.0, social networking sites such as Facebook, Twitter and LinkedIn are becoming increasingly popular. Link prediction is an important task being heavily discussed recently in the area of social networks analysis, which is to identify the future existence of links among entities in the social networks so that user experiences can be improved. In this paper, we propose a hybrid time-series link prediction model framework called DynamicNet for large social networks. Compared to existing works, our framework not only takes timing as consideration by using time-series link prediction model but also combines the strengths of topological pattern and probabilistic relational model (PRM) approaches. We evaluated our framework on three known corpora, and the favorable results indicated that our proposed approach is feasible.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Adamin, L.A., Adar, E.: Friends and neighbors on the web. Social Networks (2003)
Alwan, L.C., Roberts, H.V.: Time-series modeling for statistical process control. J. Bus. Econom. Statist. (1988)
Basilico, J., Hofmann, T.: Unifying collaborative and content-based filtering. In: ICML (2004)
Bilenko, M., Mooney, R.J.: Adaptive duplicate detection using learnable string similarity measures. In: KDD (2003)
Bilgic, M., Namata, G., Getoor, L.: Combining collective classification and link prediction. In: ICDM Workshops (2007)
Friedman, N., Getoor, L., Koller, D., Pfeffer, A.: Learning probabilistic relational models. In: IJCAI (1999)
Hoff, P., Raftery, A., Handcock, M.S.: Latent space appraches to social network analysis. Journal of the American Statistical Association (2002)
Huang, Z.: Link prediction based on graph topology: The predictive value of the generalized clustering coefficient. In: Twelfth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2006)
Huang, Z., Lin, D.K.J.: Time-series link prediction problem with applications in communication surveillance. INFORMS Journal on Computing (2009)
Jeh, G., Widom, J.: Simrank: a measure of structural-context similarity. In: KDD (2002)
Katz, L.: A new status index derived from sociometric analysis. Psychometrika (1953)
Leroy, V., Cambazoglu, B.B., Bonchi, F.: Cold start link prediction. In: KDD 2010 (2010)
Lichtenwalter, R.N., Lussier, J.T., Chawla, N.V.: New prespectives and methods in link prediction. In: KDD 2010 (2010)
Martin, S., Roe, D.C., Faulon, J.L.: Predicting protein-protein interactions using signature products. Journal of Bininformatics (2005)
Neville, J.: Statistical models and analysis techniques for learning in relational data. PhD thesis (2006)
Newman, M.E.J.: The structure and function of complex networks. SIAM Review (2003)
Nowell, D.L., Kleinberg, J.M.: The link prediction problem for social networks. In: CIKM 2003 (2003)
Nowell, D.L., Kleinberg, J.M.: The link prediction problem for social networks. JASIST (2007)
Shumway, R.H.: Applied statistical time series analysis. Prentice Hall, Englewood Cliffs (1988)
Taskar, B., Abbeel, P., Koller, D.: Discriminative probabilistic models for relational data. In: UAI (2002)
Taskar, B., Wong, M.F., Abbeel, P., Koller, D.: Link prediction in relational data. In: NIPS (2003)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Zhu, J., Xie, Q., Chin, E.J. (2012). A Hybrid Time-Series Link Prediction Framework for Large Social Network. In: Liddle, S.W., Schewe, KD., Tjoa, A.M., Zhou, X. (eds) Database and Expert Systems Applications. DEXA 2012. Lecture Notes in Computer Science, vol 7447. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32597-7_30
Download citation
DOI: https://doi.org/10.1007/978-3-642-32597-7_30
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-32596-0
Online ISBN: 978-3-642-32597-7
eBook Packages: Computer ScienceComputer Science (R0)