epl draft Hidden Link Prediction based on Node Centrality and Weak Ties

Link prediction has been widely used to extract missing information, identify spurious interactions, evaluate network evolving mechanisms, and so on. Wherein, similarity based algorithms have become the mainstream. However, most of them take into account the contributions of each common neighbor equally to the connection likelihood of two nodes. This paper proposes a model for link prediction, which is based on the node centrality of common neighbors. Three node centralities are discussed: degree, closeness and betweenness centrality. In our model, each common neighbor plays a different roles to the node connection likelihood according to their centralities. Moreover, the weak tie theory is considered for improving the prediction accuracy. Finally, extensive experiments on five real-world networks show that the proposed model can outperform the Common Neighbor (CN) and gives competitively good prediction or even better than Adamic-Adar (AA) Index and Resource Allocation (RA) Index. Introduction. – Given a snapshot of a network at time t, which new links or interactions among its members are likely to occur at time t′(t < t′)? We can formalize this question as the link prediction problem [1]. Link prediction is applicable to a variety of areas, such as proteinprotein interaction (PPI) prediction [2], identifying spurious links [3], evaluation of network evolving mechanisms [4], e-commerce [5]. Zhou et al. [6] divided the link prediction algorithms into three categories: similarity-based algorithms, maximum likelihood methods and probabilistic models. The similarity-based algorithms are the most used and they include node similarity and structural similarity. This paper will focus on node similarity algorithms. Node similarity link prediction algorithms rely on the low complexity, low time consumption and good prediction accuracy, which become the one of the most applied link prediction approaches. Wherein, Common Neighbors (CN) [7] is the widely used node similarity based algorithm. The basic assumption is that two nodes x and y, are more likely to have a link if they have many common neighbors. CN only considers the number of common neighbors. Further, many variants [8] [9] [10] of CN are proposed by taking the degrees of nodes x and y into account. Therein, Preferential Attachment index (PA) [4] is suitable for the prediction of scale-free networks, where the probability that a new link is connected to the node x and y is proportional to the degree kx and ky. Further, Adamic/Adar [11] and Zhou et al. [12] improved the CN by restraining the contributions of large degree common nodes respectively. They further improve the prediction accuracy. Most of the traditional approaches consider only the degree of each common neighbor of two nodes. They can improve the prediction accuracy, but, the improving is limited. Because the degree of node can not reflect the significance of the node completely. Murata et al. [13] gave a weighted common neighbors approach. This paper assumed that proximities between nodes could be estimated better by using both graph proximity measures and the weights of existing links in a social network. It proposed a weighted graph proximity measures and new scores that took weights of links into account. Liu et al. [14] proposed a local näıve Bayes (LNB) model for link prediction in complex network. In this model, different common neighbors will play different roles and contributes. The proposed probabilistic model is based on the Bayesian theory. The connection probability of two nodes depends on the clustering coefficients of the common neighbors. And, the author proposed the improved LNB-CN, LNB-AA, LNBRA according to näıve Bayes model. In some networks,