Abstract
Link prediction has been attached more attention in recent years. In this paper, we develop a link prediction method which has a unique perspective on using network structures. The key idea is to exploit the relationship information based on Graphical Markov models (GMM) for designing similarity indices. Specifically, networks with GMM were modeled to capture the relational influence of nodes by taking multi-hop neighbors into consideration. Then, link ties are measured for supporting relationship prediction based on the theory of weak and strong ties. Moreover, the proposed method can be used to predict the emergence of future relationships between the nodes. Finally, empirical studies on real-world dataset demonstrate that the benchmark of the proposed method improves with a significant margin compared with other methods.
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