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The weighted link prediction algorithm based on local traditional architecture only uses the topological attribute of the one-layer neighbor node. They are unable to reflect the contribution of common neighbors’ neighbor node on the potential node pair, and calculate the influence of the degree of interconnection of neighbors. This paper analyzed the contribution of the neighboring nodes on the potential pairs from the dense level of the local structure, and proposed a similarity index called( WCCLP) of the integrated weighted clustering coefficients, which could effectively enlarge the influence of the local structure on the prediction performance. Besides, WCCLP could easily be extended to the weighted local naive Bayesian link prediction model( WLNB). The experiments with unsupervised learning show that, compared with the existing local similarity algorithm, WCCLP achieves better and predictive results in many real data sets. The experimental effect of extending to WLNB also proves that weighted clustering coefficients can be effectively extended to other models. At the same time, under the classifier with supervised learning for link prediction, the feature construction with WCCLP are more discriminative than the features which derived from the existing local similarity algorithm. [ABSTRACT FROM AUTHOR]

Complex networks include biological information networks, collaboration networks and social networks. Studying the relationship prediction of complex networks helps predict relationship between proteins, find out cooperation relationship among scientists, as well as mine potential social networks. Currently, most relationship prediction algorithms are realized by similarity-based models, however ? this type of algorithms based on network topology feature are explicitly constructed, which ignore latent information behind generated relationship. To solve this problem, we propose an enhanced naive Bayesian relation prediction model （ELNB） ? which defines a conditional probability to model the local sub-graph structure. It can effectively alleviate the independence assumption of LNB and realize a quantitative calculation of neighbors contribution. Experiments on artificial datasets and real datasets show that the proposed model is better than the baselines and some recently proposed models. Meanwhile, the idea of ELNB can be extended to other similarity algorithms based on common neighbor nodes, which provides a new method for the research of such kind of model. [ABSTRACT FROM AUTHOR]