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Graph serves as a powerful tool for modeling data that has an underlying structure in non-Euclidean space, by encoding relations as edges and entities as nodes.
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The inaturalist challenge 2017 dataset
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Object-part attention model for fine-grained image classification
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Neural message passing for quantum chemistry
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Prototypical networks for few-shot learning
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Focal loss for dense object detection
Tsung-Yi Lin, Priya Goyal, Ross Girshick, Kaiming He, and Piotr Dollár · 2017
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Deep over-sampling framework for classifying imbalanced data
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Semi-supervised graph-to-graph translation
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Parameterized explainer for graph neural network
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Network embedding with completely-imbalanced labels
Zheng Wang, Xiaojun Ye, Chaokun Wang, Jian Cui, and S Yu Philip · 2020
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Multi-class imbalanced graph convolutional network learning
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Gated graph recurrent neural networks
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Deep gaussian embedding of graphs: Unsupervised inductive learning via ranking
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Graphit: Encoding graph structure in transformers
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Nrgnn: Learning a label noise resistant graph neural network on sparsely and noisily labeled graphs
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A dual-branch graph convolutional network on imbalanced node classification
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