Unveiling the unseen potential of graph learning through MLPs: Effective graph learners using propagation-embracing MLPs

Author:

Shin Yong-MinORCID,Shin Won-YongORCID

Funder

IITP

National Research Foundation of Korea

Publisher

Elsevier BV

Reference46 articles.

1. T.N. Kipf, M. Welling, Semi-Supervised Classification with Graph Convolutional Networks, in: Proc. 5th Int. Conf. Learn. Representations, ICLR, Toulon, France, 2017.

2. W.L. Hamilton, Z. Ying, J. Leskovec, Inductive Representation Learning on Large Graphs, in: Proc. 31st Int. Conf. Neural Inf. Process. Syst. (NeurIPS), 2017, pp. 1024–1034.

3. P. Velickovic, G. Cucurull, A. Casanova, A. Romero, P. Liò, Y. Bengio, Graph Attention Networks, in: Proc. 6th Int. Conf. Learn. Representations, ICLR, Vancouver, Canada, 2018.

4. K. Xu, W. Hu, J. Leskovec, S. Jegelka, How Powerful are Graph Neural Networks?, in: Proc. 7th Int. Conf. Learn. Representations, ICLR, New Orleans, LA, 2019.

5. J. Gilmer, S.S. Schoenholz, P.F. Riley, O. Vinyals, G.E. Dahl, Neural Message Passing for Quantum Chemistry, in: Proc. 34th Int. Conf. Mach. Learn., ICML, Sydney, Australia, 2017, pp. 1263–1272.

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