Link prediction based on sampling in complex networks

The link prediction problem has received extensive attention in fields such as sociology, anthropology, information science, and computer science. In many practical applications, we only need to predict the potential links between the vertices of interest, instead of predicting all of the links in a complex network. In this paper, we propose a fast similarity based approach for predicting the links related to a given node. We construct a path set connected to the given node by a random walk. The similarity score is computed within a small sub-graph formed by the path set connected to the given node, which significantly reduces the computation time. By choosing the appropriate number of sampled paths, we can restrict the error of the estimated similarities within a given threshold. Our experimental results on a number of real networks indicate that the algorithm proposed in this paper can obtain accurate results in less time than existing methods.

[1]  Ludovic Denoyer,et al.  Temporal link prediction by integrating content and structure information , 2011, CIKM '11.

[2]  Hsinchun Chen,et al.  Recommendation as link prediction in bipartite graphs: A graph kernel-based machine learning approach , 2013, Decis. Support Syst..

[3]  Andrea Munaro,et al.  The VC-dimension of graphs with respect to k-connected subgraphs , 2013, Discret. Appl. Math..

[4]  Peter L. Bartlett,et al.  Vapnik-Chervonenkis dimension of neural nets , 2003 .

[5]  Alexandre Vidmer,et al.  Prediction in complex systems: the case of the international trade network , 2015, ArXiv.

[6]  Linyuan Lü,et al.  Similarity index based on local paths for link prediction of complex networks. , 2009, Physical review. E, Statistical, nonlinear, and soft matter physics.

[7]  Alexis Papadimitriou,et al.  Fast and accurate link prediction in social networking systems , 2012, J. Syst. Softw..

[8]  Hongkun Liu,et al.  Uncovering the network evolution mechanism by link prediction , 2011 .

[9]  Dongyun Yi,et al.  Predicting link directions using local directed path , 2015 .

[10]  Yu-lin He,et al.  OWA operator based link prediction ensemble for social network , 2015, Expert Syst. Appl..

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

[12]  Yi Li,et al.  Improved bounds on the sample complexity of learning , 2000, SODA '00.

[13]  Ke-Jia Chen,et al.  A link prediction approach using semi-supervised learning in dynamic networks , 2013, 2013 Sixth International Conference on Advanced Computational Intelligence (ICACI).

[14]  Jun Li,et al.  A link prediction approach for item recommendation with complex number , 2015, Knowl. Based Syst..

[15]  Michael J. Brusco,et al.  A note on using the adjusted Rand index for link prediction in networks , 2015, Soc. Networks.

[16]  Rushed Kanawati,et al.  Supervised rank aggregation approach for link prediction in complex networks , 2012, WWW.

[17]  Zan Huang,et al.  The Time-Series Link Prediction Problem with Applications in Communication Surveillance , 2009, INFORMS J. Comput..

[18]  Hau-San Wong,et al.  Labeling of Human Motion Based on CBGA and Probabilistic Model , 2013 .

[19]  E. Xing,et al.  Discrete Temporal Models of Social Networks , 2006, SNA@ICML.

[20]  Peter L. Bartlett,et al.  Neural Network Learning - Theoretical Foundations , 1999 .

[21]  Micha Sharir,et al.  Relative (p,ε)-Approximations in Geometry , 2011, Discret. Comput. Geom..

[22]  Vladimir Vapnik,et al.  Chervonenkis: On the uniform convergence of relative frequencies of events to their probabilities , 1971 .

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

[24]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[25]  Ling Chen,et al.  Link prediction in dynamic social networks by integrating different types of information , 2014, Applied Intelligence.

[26]  Rossano Schifanella,et al.  Friendship prediction and homophily in social media , 2012, TWEB.

[27]  David Haussler,et al.  Decision Theoretic Generalizations of the PAC Model for Neural Net and Other Learning Applications , 1992, Inf. Comput..

[28]  Linyuan Lu,et al.  Link prediction based on local random walk , 2010, 1001.2467.

[29]  Francesco Buccafurri,et al.  Discovering missing me edges across social networks , 2015, Inf. Sci..

[30]  Kazem Jahanbakhsh,et al.  Predicting missing contacts in mobile social networks , 2011, 2011 IEEE International Symposium on a World of Wireless, Mobile and Multimedia Networks.

[31]  Patrick Gallinari,et al.  Probabilistic Latent Tensor Factorization Model for Link Pattern Prediction in Multi-relational Networks , 2012, ArXiv.

[32]  Buket Kaya,et al.  Age-series based link prediction in evolving disease networks , 2015, Comput. Biol. Medicine.

[33]  Zhifeng Bao,et al.  sonLP: Social network link prediction by principal component regression , 2013, 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2013).

[34]  Alain Barrat,et al.  Contact Patterns among High School Students , 2014, PloS one.

[35]  Matteo Riondato.  Sampling-Based Data Mining Algorithms: Modern Techniques and Case Studies , 2014, ECML/PKDD.