关键词: 异构信息网络, 相关性指标, 同质连接, 链路存在概率, 协同预测, 邻近性指标

Abstract: The existing link prediction methods only consider the single link type prediction or the independent prediction of multiple- link types, which often makes the prediction results are not accurate enough. To solve this problem, this paper studyies the collective prediction problem of multiple-link types in heterogeneous information networks. First of all, according to the current link information between the similar nodes of the source nodes and the similar nodes of the target nodes, the homophily connection principle is introduced and the relatedness index for different types of nodes is designed, which is used to describe the link existence probability between different types of nodes, and can be combined with the traditional proximity index to the heterogeneous link prediction. Then, the labeled and unlabeled data in heterogeneous information networks are fused, and a heterogeneous collective link prediction algorithm is proposed to predict multiple-link types collectively by obtaining complex relationships among different types of links and combining the complementary prediction information. Experimental results based on real scenes show that the proposed collective prediction approach of link can effectively improve link prediction performances in heterogeneous information networks.

Key words: heterogeneous information networks, relatedness index, homophily connection, link existence probability, collaborative prediction, proximity index