Graph classification using high-difference-frequency subgraph embedding

Author:

Gao TianchongORCID,Xu Yixin

Funder

Fundamental Research Funds for the Central Universities

National Natural Science Foundation of China

Publisher

Elsevier BV

Reference44 articles.

1. Learning convolutional neural networks for graphs;Niepert,2016

2. Martin Simonovsky, Nikos Komodakis, Dynamic edge-conditioned filters in convolutional neural networks on graphs, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2017, pp. 3693–3702.

3. Aditya Grover, Jure Leskovec, node2vec: Scalable feature learning for networks, in: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2016, pp. 855–864.

4. Weisfeiler-Lehman graph kernels;Shervashidze;J. Mach. Learn. Res.,2011

5. Hisashi Kashima, Koji Tsuda, Akihiro Inokuchi, Marginalized kernels between labeled graphs, in: Proceedings of the 20th International Conference on Machine Learning (ICML-03), 2003, pp. 321–328.

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