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Distilling high-accuracy Graph Neural Networks (GNNs) to low-latency multilayer perceptions (MLPs) on graph tasks has become a hot research topic.
Learning a similarity metric discriminatively, with application to face verification
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Collective classification in network data
Prithviraj Sen, Galileo Namata, Mustafa Bilgic, Lise Getoor, Brian Gallagher, and Tina Eliassi-Rad. 2008 · 2008
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Visualizing data using t-sne
Laurens van der Maaten and Geoffrey Hinton. 2008 · 2008
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Query-driven active surveying for collective classification
Galileo Namata, Ben London, Lise Getoor, and Bert Huang. 2012 · 2012
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Distilling the knowledge in a neural network
Geoffrey E. Hinton, Oriol Vinyals, and Jeffrey Dean. 2015 · 2015
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Inductive representation learning on large graphs
William L. Hamilton, Zhitao Ying, and Jure Leskovec. 2017 · 2017
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Semi-supervised classification with graph convolutional networks
Thomas N. Kipf and Max Welling. 2017 · 2017
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Prototypical networks for few-shot learning
Jake Snell, Kevin Swersky, and Richard S. Zemel. 2017 · 2017
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Graph attention networks
Petar Velickovic, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Liò, and Yoshua Bengio. 2017 · 2017
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Few-shot learning with graph neural networks
Victor Garcia Satorras and Joan Bruna Estrach. 2018 · 2018
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Pitfalls of graph neural network evaluation
Oleksandr Shchur, Maximilian Mumme, Aleksandar Bojchevski, and Stephan Günnemann. 2018 · 2018
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Representation learning with contrastive predictive coding
Aäron van den Oord, Yazhe Li, and Oriol Vinyals. 2018 · 2018
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Predict then propagate: Graph neural networks meet personalized pagerank
Johannes Klicpera, Aleksandar Bojchevski, and Stephan Günnemann. 2019 · 2019
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Gnnexplainer: Generating explanations for graph neural networks
Zhitao Ying, Dylan Bourgeois, Jiaxuan You, Marinka Zitnik, and Jure Leskovec. 2019 · 2019
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Measuring and relieving the over-smoothing problem for graph neural networks from the topological view
Deli Chen, Yankai Lin, Wei Li, Peng Li, Jie Zhou, and Xu Sun. 2020 · 2020
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Open graph benchmark: Datasets for machine learning on graphs
Weihua Hu, Matthias Fey, Marinka Zitnik, Yuxiao Dong, Hongyu Ren, Bowen Liu, Michele Catasta, and Jure Leskovec. 2020 · 2020
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Graph meta learning via local subgraphs
Kexin Huang and Marinka Zitnik. 2020 · 2020
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Redundancy-free computation for graph neural networks
Zhihao Jia, Sina Lin, Rex Ying, Jiaxuan You, Jure Leskovec, and Alex Aiken. 2020 · 2020
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Prototype-matching graph network for heterogeneous domain adaptation
Protognn: Prototype-assisted message passing framework for non-homophilous graphs
Yanfei Dong, Mohammed Haroon Dupty, Lambert Deng, Yong Liang Goh, and Wee Sun Lee. 2022 · 2022
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Federated graph neural networks: Overview, techniques and challenges
Rui Liu and Han Yu. 2022 · 2022
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PAGE: prototype-based model-level explanations for graph neural networks
Yong-Min Shin, Sun-Woo Kim, and Won-Yong Shin. 2022 · 2022
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NOSMOG: learning noise-robust and structure-aware mlps on graphs
Yijun Tian, Chuxu Zhang, Zhichun Guo, Xiangliang Zhang, and Nitesh V. Chawla. 2022 · 2022
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Distance-wise prototypical graph neural network for imbalanced node classification
Yu Wang, Charu Aggarwal, and Tyler Derr. 2022 · 2022
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Teaching yourself: Graph self-distillation on neighborhood for node classification
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Zijian Wang, Yadan Luo, Zi Huang, and Mahsa Baktashmotlagh. 2020 · 2020
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Graph few-shot learning via knowledge transfer
Huaxiu Yao, Chuxu Zhang, Ying Wei, Meng Jiang, Suhang Wang, Junzhou Huang, Nitesh V. Chawla, and Zhenhui Li. 2020 · 2020
Cited alongside, same era.
Graph-mlp: Node classification without message passing in graph
Yang Hu, Haoxuan You, Zhecan Wang, Zhicheng Wang, Erjin Zhou, and Yue Gao. 2021 · 2021
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Large scale learning on non-homophilous graphs: New benchmarks and strong simple methods
Derek Lim, Felix Hohne, Xiuyu Li, Sijia Linda Huang, Vaishnavi Gupta, Omkar Bhalerao, and Ser-Nam Lim. 2021 · 2021
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A unified view on graph neural networks as graph signal denoising
Yao Ma, Xiaorui Liu, Tong Zhao, Yozen Liu, Jiliang Tang, and Neil Shah. 2021 · 2021
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Zero-shot node classification with decomposed graph prototype network
Zheng Wang, Jialong Wang, Yuchen Guo, and Zhiguo Gong. 2021 · 2021
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Interpreting and unifying graph neural networks with an optimization framework
Meiqi Zhu, Xiao Wang, Chuan Shi, Houye Ji, and Peng Cui. 2021 · 2021
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Lirong Wu, Jun Xia, Haitao Lin, Zhangyang Gao, Zicheng Liu, Guojiang Zhao, and Stan Z. Li. 2022 · 2022
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Graph-less neural networks: Teaching old mlps new tricks via distillation
Shichang Zhang, Yozen Liu, Yizhou Sun, and Neil Shah. 2022a · 2022
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Reasoning on graphs: Faithful and interpretable large language model reasoning
Linhao Luo, Yuan-Fang Li, Gholamreza Haffari, and Shirui Pan. 2023 · 2023
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Graph-guided reasoning for multi-hop question answering in large language models
Jinyoung Park, Ameen Patel, Omar Zia Khan, Hyunwoo J Kim, and Joo-Kyung Kim. 2023 · 2023
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Interpretable prototype-based graph information bottleneck
Sangwoo Seo, Sungwon Kim, and Chanyoung Park. 2023 · 2023
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Llmrec: Large language models with graph augmentation for recommendation
Wei Wei, Xubin Ren, Jiabin Tang, Qinyong Wang, Lixin Su, Suqi Cheng, Junfeng Wang, Dawei Yin, and Chao Huang. 2023 · 2023
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Adagmlp: Adaboosting gnn-to-mlp knowledge distillation
Weigang Lu, Ziyu Guan, Wei Zhao, and Yaming Yang. 2024 · 2071
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