Heterogeneous Information Network Representation Learning Framework Based on Graph Attention Network

Shize KANG; Lixin JI; Jianpeng ZHANG

doi:10.11999/JEIT200034

Volume 43 Issue 4

Apr. 2021

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Shize KANG, Lixin JI, Jianpeng ZHANG. Heterogeneous Information Network Representation Learning Framework Based on Graph Attention Network[J]. Journal of Electronics & Information Technology, 2021, 43(4): 915-922. doi: 10.11999/JEIT200034

Citation:

Shize KANG, Lixin JI, Jianpeng ZHANG. Heterogeneous Information Network Representation Learning Framework Based on Graph Attention Network[J]. Journal of Electronics & Information Technology, 2021, 43(4): 915-922. doi: 10.11999/JEIT200034

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Heterogeneous Information Network Representation Learning Framework Based on Graph Attention Network

doi: 10.11999/JEIT200034

Information Engineering University, Zhengzhou 450001, China

Funds: The National Natural Science Foundation of China (61521003)

Received Date: 2020-01-09
Rev Recd Date: 2020-12-06

Available Online: 2020-12-15

Publish Date: 2021-04-20

Abstract

Abstract

Commonly used heterogeneous information networks include knowledge graphs and heterogeneous information networks with simple schemas. Their representation learning follows usually different methods. The similarities and differences between knowledge graphs and heterogeneous information networks with simple schemas are summarized, and a general heterogeneous information network representation learning framework is proposed. The proposed framework can be divided into three parts: the basic vector model, the graph attention network based propagation model, and the task model. The basic vector model is used to learn basic network vector; The propagation model learns the high-order neighbor features of the network by stacking attention layers. The replaceable task module is suitable for different application scenarios. Compared with the benchmark model, the proposed framework achieves relatively good results in the link prediction task of the knowledge graph and the node classification task of the heterogeneous information network.
- Heterogeneous information network,
- Knowledge graph,
- Graph attention network,
- Representation learning

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References(14)

References

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