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.
Fund:Supported by National Science and Technology Support Plan Project(No.2013BAH21B02-01),Natural Science Foundation of Beijing(No.4153058)
About author:: (DU Yongping(Corresponding author), Ph.D., associate professor. Her research interests include information retrieval and natural language processing.) (LIU Jingxuan, born in 1992, master student. Her research interests include natural language processing.) (ZHANG Jinli, born in 1986, Ph.D. candidate. Her research interests include natural language processing.)
DU Yongping,LIU Jingxuan,ZHANG Jinli. Multi-semantic Metapath Based Classification Method in Heterogeneous Information Network[J]. , 2017, 30(12): 1100-1107.
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