Neural predictor-based automated graph classifier framework
Author:
Funder
National Natural Science Foundation of China
Publisher
Springer Science and Business Media LLC
Subject
Artificial Intelligence,Software
Link
https://link.springer.com/content/pdf/10.1007/s10994-022-06287-5.pdf
Reference51 articles.
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2. Ben Jemaa, Z., Fournier-Prunaret, D., & Belghith, S. (2015). Kendall’s tau based correlation analysis of chaotic sequences generated by piecewise linear maps. In International Journal of Bifurcation and Chaos, 25(13), 1550177.
3. Borgwardt, KM., Ong, C S, Schönauer, S, Vishwanathan, S. V. N., Smola, A J., & Kriegel, H-P. (2005). Protein function prediction via graph kernels. In thirteenth international conference on intelligent systems for molecular biology, pp. 47–56.
4. Bronstein, M. M., Bruna, J., LeCun, Y., Szlam, A., & Vandergheynst, P. (2017). Geometric deep learning: Going beyond euclidean data. IEEE Signal Processing Magazine, 34(4), 18–42.
5. Cai, S, Li, L, Deng, J, Zhang, B, Zha, Zheng-J, Su, L, & Huang, Q. (2021). Rethinking graph neural architecture search from message-passing. In IEEE conference on computer vision and pattern recognition, CVPR 2021, virtual, June 19–25, 2021, pp. 6657–6666. Computer Vision Foundation / IEEE.
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