Heterogeneous information network representation learning based on transition probability matrix (HINtpm)

被引：0

作者：

Zhao T.-T. ^{[1
]}

Wang Z. ^{[1
]}

Lu Y.-N. ^{[1
]}

机构：

[1] College of Computer Science and Technology, Key Laboratory of Symbolic Computation and Knowledge Engineering, Jilin University, Changchun

来源：

Zhejiang Daxue Xuebao (Gongxue Ban)/Journal of Zhejiang University (Engineering Science) | 2019年 / 53卷 / 03期

关键词：

Auto-encoder; Heterogeneous information network (HIN); Meta-path; Network representation learning; Nodes' similarity; Transition probability matrix;

D O I：

10.3785/j.issn.1008-973X.2019.03.016

中图分类号：

学科分类号：

摘要：

First, the final probability transition matrix was obtained by combining the first-order and second-order similarity of the nodes, according to the meta-path and the commuting matrix. Then, a Denoisin Auto-encoder was used to reduce the dimension of probability transition matrix for getting the node representation in heterogeneous information network. Finally, the node representation in heterogeneous information network was classified by gradient boosting decision tree (GBDT) and the classification accuracy under different percentage training set was obtained. Use the clustering index normalized mutual information (NMI) to evaluate the clustering effect and use T-SNE to show the visual effect. Experiments were performed on data sets DBLP and AMiner. The proposed heterogeneous information network representation learning based on transition probability matrix (HINtpm) was compared with DeepWalk, node2vec and metapath2vec methods. As results, compared with DeepWalk method, HINtpm improved the classification accuracy by 24% the maximum on the application task-node classify and increased the clustering index NMI by 13% the maximum. © 2019, Zhejiang University Press. All right reserved.

引用

页码：548 / 554

页数：6

共 22 条

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