Negative sampling strategies for contrastive self-supervised learning of graph representations

Author:

Hafidi Hakim,Ghogho Mounir,Ciblat Philippe,Swami Ananthram

Publisher

Elsevier BV

Subject

Electrical and Electronic Engineering,Computer Vision and Pattern Recognition,Signal Processing,Software,Control and Systems Engineering

Reference49 articles.

1. Diffusion-convolutional neural networks;Atwood,2016

2. T. N. Kipf, M. Welling, Semi-supervised classification with graph convolutional networks, (2016). arXiv preprint arXiv:1609.02907.

3. Geometric deep learning: going beyond euclidean data;Bronstein;IEEE Signal Process. Mag.,2017

4. K. Xu, W. Hu, J. Leskovec, S. Jegelka, How powerful are graph neural networks?, (2018). arXiv preprint arXiv:1810.00826.

5. Inductive representation learning on large graphs;Hamilton,2017

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