Research of link prediction algorithmic based on semi-supervisor learning
基于半监督学习的链接预测算法的研究

It is very hard to forecast about structure of network in link mining. To slove the problem, this paper proposed a new semi-supervisor learning algorithmic based on an accelerated conjugate gradient method and link similarity delivery proliferation, by using auxiliary information such as node similarity to predict the unknown structure. Used the tensor to represent the multidimensional complexity multi-relation data, calculated the similarity of tensors by Kronecker product and Kronecker sum, reduced the complexity of the compute time and RAM. The effectiveness and robustness of the algorithmic was tested in social networks and biological networks.