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GNN-to-MLP distillation aims to utilize knowledge distillation (KD) to learn computationally-efficient multi-layer perceptron (student MLP) on graph data by mimicking the output representations of teacher GNN.
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Variational graph auto-encoders
Thomas N Kipf and Max Welling · 2016
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Neural machine translation of rare words with subword units
Rico Sennrich, Barry Haddow, and Alexandra Birch · 2016
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William L. Hamilton, Rex Ying, and Jure Leskovec · 2017
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Adaptive sampling towards fast graph representation learning
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Pitfalls of graph neural network evaluation
Oleksandr Shchur, Maximilian Mumme, Aleksandar Bojchevski, and Stephan Günnemann · 2018
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Graph attention networks
Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Liò, and Yoshua Bengio · 2018
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Graph convolutional neural networks for web-scale recommender systems
Rex Ying, Ruining He, Kaifeng Chen, Pong Eksombatchai, William L Hamilton, and Jure Leskovec · 2018
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Mixhop: Higher-order graph convolutional architectures via sparsified neighborhood mixing
Sami Abu-El-Haija, Bryan Perozzi, Amol Kapoor, Nazanin Alipourfard, Kristina Lerman, Hrayr Harutyunyan, Greg Ver Steeg, and Aram Galstyan · 2019
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Mincut pooling in graph neural networks
Filippo Maria Bianchi, Daniele Grattarola, and Cesare Alippi · 2019
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Beyond homophily in graph neural networks: Current limitations and effective designs, 2020
Jiong Zhu, Yujun Yan, Lingxiao Zhao, Mark Heimann, Leman Akoglu, and Danai Koutra · 2020
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Graph-free knowledge distillation for graph neural networks, 2021
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Gnnautoscale: Scalable and expressive graph neural networks via historical embeddings
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Knowledge distillation: A survey
Jianping Gou, Baosheng Yu, Stephen J Maybank, and Dacheng Tao · 2021
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Graph-mlp: node classification without message passing in graph
Yang Hu, Haoxuan You, Zhecan Wang, Zhicheng Wang, Erjin Zhou, and Yue Gao · 2021
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Cluster-gcn: An efficient algorithm for training deep and large graph convolutional networks
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Graph neural networks for social recommendation
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Scaling graph neural networks with approximate pagerank
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Combining label propagation and simple models out-performs graph neural networks
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Neighborhood reconstructing autoencoders
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Pick and choose: a gnn-based imbalanced learning approach for fraud detection
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Rethinking soft labels for knowledge distillation: A bias–variance tradeoff perspective
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Node feature extraction by self-supervised multi-scale neighborhood prediction
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Revisiting heterophily for graph neural networks, 2022
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Graph auto-encoder via neighborhood wasserstein reconstruction
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Graph neural networks in recommender systems: a survey
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Omni-granular ego-semantic propagation for self-supervised graph representation learning
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From stars to subgraphs: Uplifting any gnn with local structure awareness
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Cold brew: Distilling graph node representations with incomplete or missing neighborhoods
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Lmc: Fast training of gnns via subgraph sampling with provable convergence
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Individual and structural graph information bottlenecks for out-of-distribution generalization
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