A Parameterized Probabilistic Model of Network Evolution for Supervised Link Prediction

We introduce a new approach to the problem of link prediction for network structured domains, such as the Web, social networks, and biological networks. Our approach is based on the topological features of network structures, not on the node features. We present a novel parameterized probabilistic model of network evolution and derive an efficient incremental learning algorithm for such models, which is then used to predict links among the nodes. We show some promising experimental results using biological network data sets.

[1]  Hsinchun Chen,et al.  Link prediction approach to collaborative filtering , 2005, Proceedings of the 5th ACM/IEEE-CS Joint Conference on Digital Libraries (JCDL '05).

[2]  Jon Kleinberg,et al.  The link prediction problem for social networks , 2003, CIKM '03.

[3]  John D. Lafferty,et al.  Diffusion Kernels on Graphs and Other Discrete Input Spaces , 2002, ICML.

[4]  M. Newman.  Clustering and preferential attachment in growing networks. , 2001, Physical review. E, Statistical, nonlinear, and soft matter physics.

[5]  A. Barabasi,et al.  Evolution of the social network of scientific collaborations , 2001, cond-mat/0104162.

[6]  William Stafford Noble,et al.  Learning kernels from biological networks by maximizing entropy , 2004, ISMB/ECCB.

[7]  Lyle H. Ungar,et al.  Statistical Relational Learning for Link Prediction , 2003 .

[8]  Leo Katz,et al.  A new status index derived from sociometric analysis , 1953 .

[9]  Yoram Singer,et al.  A Comparison of New and Old Algorithms for a Mixture Estimation Problem , 2004, Machine Learning.

[10]  Lada A. Adamic,et al.  Friends and neighbors on the Web , 2003, Soc. Networks.

[11]  Lise Getoor,et al.  Link mining: a survey , 2005, SKDD.

[12]  Reka Albert,et al.  Mean-field theory for scale-free random networks , 1999 .

[13]  Yoshihiro Yamanishi,et al.  Supervised enzyme network inference from the integration of genomic data and chemical information , 2005, ISMB.

[14]  Ben Taskar,et al.  Link Prediction in Relational Data , 2003, NIPS.

[15]  Padhraic Smyth,et al.  Prediction and ranking algorithms for event-based network data , 2005, SKDD.

[16]  B. Snel,et al.  Comparative assessment of large-scale data sets of protein–protein interactions , 2002, Nature.

[17]  Susumu Goto,et al.  The KEGG resource for deciphering the genome , 2004, Nucleic Acids Res..

[18]  Eli Upfal,et al.  Stochastic models for the Web graph , 2000, Proceedings 41st Annual Symposium on Foundations of Computer Science.

[19]  Jon M. Kleinberg,et al.  The Web as a Graph: Measurements, Models, and Methods , 1999, COCOON.

[20]  Mohammad Al Hasan,et al.  Link prediction using supervised learning , 2006 .