Integrating Latent Feature Model and Kernel Function for Link Prediction in Bipartite Networks

Link prediction aims to infer missing links or predict future links from existing network structure. In recent years, most studies of link prediction mainly focus on monopartite networks. However, a class of complex systems can be represented by bipartite networks, which containing two different types of nodes and the no links exist in the same type. In this paper, we propose Kernel-based Latent Feature Models (KLFM) framework which can extract nonlinear high-order information in the existing network through kernel-based mappings. Then a kernel-based iterative rule has been developed. Extensive experiments on eight disparate real-world bipartite networks demonstrate that the KLFM framework achieves a more robust and explicable performance than other methods.

[1]  Ling Chen,et al.  Projection-based link prediction in a bipartite network , 2017, Inf. Sci..

[2]  Caroline O. Buckee,et al.  A Network Approach to Analyzing Highly Recombinant Malaria Parasite Genes , 2013, PLoS Comput. Biol..

[3]  Pengfei Jiao,et al.  Similarity-based Regularized Latent Feature Model for Link Prediction in Bipartite Networks , 2017, Scientific Reports.

[4]  Yi-Cheng Zhang,et al.  Bipartite network projection and personal recommendation. , 2007, Physical review. E, Statistical, nonlinear, and soft matter physics.

[5]  Yoshihiro Yamanishi,et al.  DINIES: drug–target interaction network inference engine based on supervised analysis , 2014, Nucleic Acids Res..

[6]  Yoshihiro Yamanishi,et al.  Prediction of drug–target interaction networks from the integration of chemical and genomic spaces , 2008, ISMB.

[7]  H. Sebastian Seung,et al.  Algorithms for Non-negative Matrix Factorization , 2000, NIPS.

[8]  Michele Coscia,et al.  The Structure and Dynamics of International Development Assistance , 2013 .

[9]  Linyuan Lu,et al.  Link Prediction in Complex Networks: A Survey , 2010, ArXiv.

[10]  Charles Elkan,et al.  Link Prediction via Matrix Factorization , 2011, ECML/PKDD.

[11]  Jonathan L. Herlocker,et al.  Evaluating collaborative filtering recommender systems , 2004, TOIS.

[12]  Michele Coscia,et al.  Using Random Walks to Generate Associations between Objects , 2014, PloS one.

[13]  J. Hanley,et al.  The meaning and use of the area under a receiver operating characteristic (ROC) curve. , 1982, Radiology.

[14]  Simone Daminelli,et al.  Common neighbours and the local-community-paradigm for topological link prediction in bipartite networks , 2015, ArXiv.

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

[16]  Michael Schroeder,et al.  Pioneering topological methods for network-based drug–target prediction by exploiting a brain-network self-organization theory , 2017, Briefings Bioinform..