Heterogeneous graph attention network with motif clique

Author:

Wang ChenxuORCID,Luo Minnan,Peng Zhen,Dong Yixiang,Liu Huaping

Funder

China Knowledge Centre for Engineering Sciences and Technology

K. C. Wong Education Foundation

Chinese Academy of Engineering

Ministry of Education of the People's Republic of China

National Natural Science Foundation of China

Publisher

Elsevier BV

Subject

Artificial Intelligence,Cognitive Neuroscience,Computer Science Applications

Reference38 articles.

1. T. Hamaguchi, H. Oiwa, M. Shimbo, Y. Matsumoto, Knowledge Transfer for Out-of-Knowledge-Base Entities : A Graph Neural Network Approach, in: Proceedings of the 26th International Joint Conference on Artificial Intelligence, 2017, pp. 1802–1808.

2. Spatial temporal incidence dynamic graph neural networks for traffic flow forecasting;Peng;Inform. Sci.,2020

3. Multi-attributed heterogeneous graph convolutional network for bot detection;Zhao;Inform. Sci.,2020

4. T.N. Kipf, M. Welling, Semi-Supervised Classification with Graph Convolutional Networks, in: Proceedings of the International Conference on Learning Representations, 2017.

5. P. Velickovic, G. Cucurull, A. Casanova, A. Romero, P. Liò, Y. Bengio, Graph Attention Networks, in: Proceedings of the International Conference on Learning Representations, 2018.

Cited by 3 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Hybrid-attention mechanism based heterogeneous graph representation learning;Expert Systems with Applications;2024-09

2. Heterogeneous Network Motif Coding, Counting, and Profiling;ACM Transactions on Knowledge Discovery from Data;2024-08-28

3. Efficient Maximal Motif-Clique Enumeration over Large Heterogeneous Information Networks;Proceedings of the VLDB Endowment;2024-07

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