Graph convolutional network with multi-similarity attribute matrices fusion for node classification
Author:
Funder
Young Scientists Fund
Key Programme
Publisher
Springer Science and Business Media LLC
Subject
Artificial Intelligence,Software
Link
https://link.springer.com/content/pdf/10.1007/s00521-021-06429-1.pdf
Reference44 articles.
1. Sun K, Zhu Z, Lin Z (2020) Multi-stage self-supervised learning for graph convolutional networks. In: Proceedings of 34th AAAI conference on artificial intelligence (AAAI 2020), AAAI, pp 5892–5899
2. Zhao J, Zhou Z, Guan Z, et al (2019) Intentgc: a scalable graph convolution framework fusing heterogeneous information for recommendation. In: Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery and data mining (SIGKDD 2019), ACM, pp 2347–2357
3. Huang Q, Wei J, Cai Y et al (2020) Aligned dual channel graph convolutional network for visual question answering. In: Proceedings of the 58th annual meeting of the association for computational linguistics (ACL 2020), ACL, pp 7166–7176
4. Qiao L, Zhao H, Huang X et al (2019) A structure-enriched neural network for network embedding. Exp Syst Appl 117:300–311
5. Gao H, Wang Z, Ji S (2018) Large-scale learnable graph convolutional networks. In: Proceedings of the 24th ACM SIGKDD international conference on knowledge discovery and data mining (SIGKDD 2018), ACM, pp 1416–1424
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