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In attribute network, the attribute information associated with nodes is essential to improve the performance of network embedding tasks. Nevertheless, network is a graph structure, in which nodes not only contain attribute information but also embrace the rich structural information. In order to make full use of the structural information, firstly, this paper defined influential node characteristics, spatial relationships, and constructed similarity matrix based on the definition of link prediction. Then it mapped correlation similarity vector associated with nodes in the binary group to the relationship space of the adjacency matrix, so as to maintain the node vector matrix structure information feature. Based on the definition of normalized graph Laplacian, it fused the attribute information and label feature and integrated the above three kinds of information into an optimization framework. Finally, it inferenced the projection matrix by calculating the local maximum value through a second order derivative. Experimental results indicate that the proposed algorithm can effectively utilize information of the adjacency structure with the binary group of nodes, and compared with other benchmark network embedding algorithms, it also can achieve better results on the node classification task. [ABSTRACT FROM AUTHOR]

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]

Collaboration relation analysis and prediction aims to predict collaboration between scholars according to the network's structural information. It is of great significance to information diffusion and dynamic changes. This paper proposed a new algorithm based on the community structure to improve the classic prediction metric which incorporating the topological properties. Firstly, it gave links concurrent distribution under community detection algorithm in collaboration network and theoretical basis for model construction, and then proposed an improved predicting model based on community topology information. Finally, it used different community discovery algorithms for extensive experiment. The experimental results show that the effect and performance of this method is better than some classical algorithms. Moreover, such algorithm can effectively introduce community structure information on the collaboration prediction in real scientific research network and provide a new idea for analysis and forecast of such prediction problem. [ABSTRACT FROM AUTHOR]