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Graph Neural Networks (GNNs) have risen to prominence in learning representations for graph structured data.
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Predict then propagate: Graph neural networks meet personalized pagerank
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Dropedge: Towards deep graph convolutional networks on node classification. In International Conference on Learning Representations
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Adversarial examples on graph data: Deep insights into attack and defense
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Pairnorm: Tackling oversmoothing in gnns
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Adversarial attacks on graph neural networks via meta learning
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Graph Unrolling Networks: Interpretable Neural Networks for Graph Signal Denoising
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Hongbin Pei, Bingzhe Wei, Kevin Chen-Chuan Chang, Yu Lei, and Bo Yang. 2020 · 2020
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Scale-Free, Attributed and Class-Assortative Graph Generation to Facilitate Introspection of Graph Neural Networks
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Data Augmentation for Graph Neural Networks
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Is Homophily a Necessity for Graph Neural Networks?
Yao Ma, Xiaorui Liu, Neil Shah, and Jiliang Tang. 2021 · 2021
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Interpreting and Unifying Graph Neural Networks with An Optimization Framework
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