A Method of Link Prediction Based on Betweenness

Link prediction in complex networks has attracted increasing attention of researchers in many domains. The prediction methods are usually used to find missing information, identify spurious interactions, and reconstruct networks. Inspired by the rich-get-richer mechanism, we propose a novel index on the basis of betweenness. Extensive experiments show that the proposed method performs well on some networks. Especially, on the Adjnoun network and Florida network, it outperforms some mainstream link prediction baselines, such as CN Index, AA Index and RA Index.

[1]  Roger Guimerà,et al.  Extracting the hierarchical organization of complex systems , 2007, Proceedings of the National Academy of Sciences.

[2]  Zheng Xie,et al.  Potential links by neighbor communities , 2014 .

[3]  U. Brandes.  A faster algorithm for betweenness centrality , 2001 .

[4]  Jianping Li,et al.  Link Prediction via Convex Nonnegative Matrix Factorization on Multiscale Blocks , 2014, J. Appl. Math..

[5]  Carlos Melián,et al.  FOOD WEB COHESION , 2004 .

[6]  M E J Newman,et al.  Community structure in social and biological networks , 2001, Proceedings of the National Academy of Sciences of the United States of America.

[7]  L. Freeman.  Centrality in social networks conceptual clarification , 1978 .

[8]  A Díaz-Guilera,et al.  Self-similar community structure in a network of human interactions. , 2003, Physical review. E, Statistical, nonlinear, and soft matter physics.

[9]  M E J Newman.  Assortative mixing in networks. , 2002, Physical review letters.

[10]  W. Zachary,et al.  An Information Flow Model for Conflict and Fission in Small Groups , 1977, Journal of Anthropological Research.

[11]  Linyuan Lü,et al.  Predicting missing links via local information , 2009, 0901.0553.

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

[13]  M. Newman,et al.  Hierarchical structure and the prediction of missing links in networks , 2008, Nature.

[14]  Edoardo M. Airoldi,et al.  Mixed Membership Stochastic Blockmodels , 2007, NIPS.

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

[16]  R. Hanneman.  Introduction to Social Network Methods , 2001 .

[17]  David J. Hand,et al.  A Simple Generalisation of the Area Under the ROC Curve for Multiple Class Classification Problems , 2001, Machine Learning.

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

[19]  M. Newman,et al.  Finding community structure in networks using the eigenvectors of matrices. , 2006, Physical review. E, Statistical, nonlinear, and soft matter physics.

[20]  Timothy A. Davis,et al.  The university of Florida sparse matrix collection , 2011, TOMS.

[21]  Duncan J. Watts,et al.  Collective dynamics of ‘small-world’ networks , 1998, Nature.