An improved link prediction algorithm based on common neighbors index with community membership information

human life is more and more dependent on the safety, reliability and effective operation of a variety of complex networks; however, most of networks are sparse, which means the network data is incomplete. To solve the problem, various link prediction methods have been proposed to find missing links in given networks. Among these methods, similarity-based methods are effective, however, still imperfect. In order to improve the predicting results, we combined local and global information of the network and then proposed a method based on one of similarity-based methods with community information. Experiments results show that the inclusion of community information improves the accuracy of results of predicting missing links.

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

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

[3]  A RAPOPORT,et al.  A study of a large sociogram. , 2007 .

[4]  Jean-Loup Guillaume,et al.  Fast unfolding of communities in large networks , 2008, 0803.0476.

[5]  M. Newman,et al.  Finding community structure in very large networks. , 2004, Physical review. E, Statistical, nonlinear, and soft matter physics.

[6]  Aurora E. Clark,et al.  ChemNetworks: A complex network analysis tool for chemical systems , 2014, J. Comput. Chem..

[7]  Sune Lehmann,et al.  Link communities reveal multiscale complexity in networks , 2009, Nature.

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

[9]  R. Rosenfeld.  Nature , 2009, Otolaryngology--head and neck surgery : official journal of American Academy of Otolaryngology-Head and Neck Surgery.

[10]  David Liben-Nowell,et al.  The link-prediction problem for social networks , 2007 .

[11]  Réka Albert,et al.  Structural vulnerability of the North American power grid. , 2004, Physical review. E, Statistical, nonlinear, and soft matter physics.

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

[13]  R. Albert,et al.  The large-scale organization of metabolic networks , 2000, Nature.

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

[15]  H. White,et al.  “Structural Equivalence of Individuals in Social Networks” , 2022, The SAGE Encyclopedia of Research Design.

[16]  Gueorgi Kossinets.  Effects of missing data in social networks , 2006, Soc. Networks.

[17]  Giuseppe Serazzi,et al.  Computer Virus Propagation Models , 2003, MASCOTS Tutorials.

[18]  T. Vicsek,et al.  Uncovering the overlapping community structure of complex networks in nature and society , 2005, Nature.

[19]  Jianping Li,et al.  A prediction method enhanced by the degree of nodes , 2013, 2013 Ninth International Conference on Natural Computation (ICNC).

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

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

[22]  M E J Newman,et al.  Finding and evaluating community structure in networks. , 2003, Physical review. E, Statistical, nonlinear, and soft matter physics.

[23]  John E. Hopcroft,et al.  Using community information to improve the precision of link prediction methods , 2012, WWW.

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

[25]  D. Lusseau,et al.  The bottlenose dolphin community of Doubtful Sound features a large proportion of long-lasting associations , 2003, Behavioral Ecology and Sociobiology.