Abstract：Heterogeneous information network(HIN) is a kind of large-scale network containing many types of objects and complex links. The metapath based object classification method in HIN is proposed in this paper. The correlation feature matrix between nodes is built by the use of the metapath with different semantic information. In addition, the jumping path is extended to solve the problem of information sparseness. The experiments are conducted on DBLP dataset and the results show high performance of the proposed method in the complex network by using fewer labeled data. Furthermore, t-test result denotes that the performance is improved significantly by jumping path with small labeled data.
杜永萍，刘京旋，张津丽. 基于多语义元路径的异质网节点分类方法*[J]. 模式识别与人工智能, 2017, 30(12): 1100-1107.
DU Yongping, LIU Jingxuan, ZHANG Jinli. Multi-semantic Metapath Based Classification Method in Heterogeneous Information Network. , 2017, 30(12): 1100-1107.
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