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Graph Neural Networks (GNNs) are deep-learning architectures designed for graph-type data, where understanding relationships among individual observations is crucial.
A hypercube-based encoding for evolving large-scale neural networks
Kenneth O Stanley, David B D’Ambrosio, and Jason Gauci · 2009
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Practical bayesian optimization of machine learning algorithms
Jasper Snoek, Hugo Larochelle, and Ryan P Adams · 2012
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Convolutional neural networks on graphs with fast localized spectral filtering
Michaël Defferrard, Xavier Bresson, and Pierre Vandergheynst · 2016
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David Ha, Andrew Dai, and Quoc V Le · 2016
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Variational graph auto-encoders
Thomas N Kipf and Max Welling · 2016
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Revisiting semi-supervised learning with graph embeddings
Zhilin Yang, William Cohen, and Ruslan Salakhudinov · 2016
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Semi-supervised classification with graph convolutional networks
Thomas N. Kipf and Max Welling · 2017
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Generating neural networks with neural networks
Lior Deutsch · 2018
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Bilevel programming for hyperparameter optimization and meta-learning
Luca Franceschi, Paolo Frasconi, Saverio Salzo, Riccardo Grazzi, and Massimiliano Pontil · 2018
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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 Lio, and Yoshua Bengio · 2018
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Predict then propagate: Graph neural networks meet personalized pagerank
Johannes Gasteiger, Aleksandar Bojchevski, and Stephan Günnemann · 2019
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Inductive representation learning on large graphs
Will Hamilton, Zhitao Ying, and Jure Leskovec · 2019
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Decoupled weight decay regularization
Ilya Loshchilov and Frank Hutter · 2019
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Hypergan: A generative model for diverse, performant neural networks
Neale Ratzlaff and Li Fuxin · 2019
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How powerful are graph neural networks?
Keyulu Xu, Weihua Hu, Jure Leskovec, and Stefanie Jegelka · 2019
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Graph hypernetworks for neural architecture search
Chris Zhang, Mengye Ren, and Raquel Urtasun · 2019
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Graph neural architecture search
Yang Gao, Hong Yang, Peng Zhang, Chuan Zhou, and Yue Hu · 2020
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Strategies for pre-training graph neural networks
Weihua Hu, Bowen Liu, Joseph Gomes, Marinka Zitnik, Percy Liang, Vijay Pande, and Jure Leskovec · 2020
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Gpt-gnn: Generative pre-training of graph neural networks
Ziniu Hu, Yuxiao Dong, Kuansan Wang, Kai-Wei Chang, and Yizhou Sun · 2020
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Geom-gcn: Geometric graph convolutional networks
Hongbin Pei, Bingzhe Wei, Kevin Chen-Chuan Chang, Yu Lei, and Bo Yang · 2020
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Predicting neural network accuracy from weights
Thomas Unterthiner, Daniel Keysers, Sylvain Gelly, Olivier Bousquet, and Ilya Tolstikhin · 2020
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A comprehensive survey on graph neural networks
Zonghan Wu, Shirui Pan, Fengwen Chen, Guodong Long, Chengqi Zhang, and S Yu Philip · 2020
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Design space for graph neural networks
Learning-rate-free learning by d-adaptation
Aaron Defazio and Konstantin Mishchenko · 2023
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Hyperdiffusion: Generating implicit neural fields with weight-space diffusion
Ziya Erkoç, Fangchang Ma, Qi Shan, Matthias Nießner, and Angela Dai · 2023
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Dog is sgd’s best friend: A parameter-free dynamic step size schedule
Maor Ivgi, Oliver Hinder, and Yair Carmon · 2023
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Towards deep attention in graph neural networks: Problems and remedies
Soo Yong Lee, Fanchen Bu, Jaemin Yoo, and Kijung Shin · 2023
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A magnetic framelet-based convolutional neural network for directed graphs
Lequan Lin and Junbin Gao · 2023
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Jiaxuan You, Zhitao Ying, and Jure Leskovec · 2020
Cited alongside, same era.
Graph neural networks: A review of methods and applications
Jie Zhou, Ganqu Cui, Shengding Hu, Zhengyan Zhang, Cheng Yang, Zhiyuan Liu, Lifeng Wang, Changcheng Li, and Maosong Sun · 2020
Cited alongside, same era.
Beyond homophily in graph neural networks: Current limitations and effective designs
Jiong Zhu, Yujun Yan, Lingxiao Zhao, Mark Heimann, Leman Akoglu, and Danai Koutra · 2020
Cited alongside, same era.
Learning to pre-train graph neural networks
Yuanfu Lu, Xunqiang Jiang, Yuan Fang, and Chuan Shi · 2021
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Multi-scale attributed node embedding
Benedek Rozemberczki, Carl Allen, and Rik Sarkar · 2021
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Self-supervised representation learning on neural network weights for model characteristic prediction
Konstantin Schürholt, Dimche Kostadinov, and Damian Borth · 2021
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Meta-learning via classifier(-free) guidance
Elvis Nava, Seijin Kobayashi, Yifei Yin, Robert K. Katzschmann, and Benjamin F Grewe · 2022
Cited alongside, same era.
Konstantin Mishchenko and Aaron Defazio · 2023
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A survey on oversmoothing in graph neural networks
T Konstantin Rusch, Michael M Bronstein, and Siddhartha Mishra · 2023
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Zhiqi Shao, Dai Shi, Andi Han, Yi Guo, Qibin Zhao, and Junbin Gao · 2023
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How curvature enhance the adaptation power of framelet gcns
Dai Shi, Yi Guo, Zhiqi Shao, and Junbin Gao · 2023
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A simple yet effective framelet-based graph neural network for directed graphs
Chunya Zou, Andi Han, Lequan Lin, Ming Li, and Junbin Gao · 2023
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From continuous dynamics to graph neural networks: Neural diffusion and beyond
Andi Han, Dai Shi, Lequan Lin, and Junbin Gao · 2024
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Design your own universe: A physics-informed agnostic method for enhancing graph neural networks
Dai Shi, Andi Han, Lequan Lin, Yi Guo, Zhiyong Wang, and Junbin Gao · 2024
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Revisiting generalized p-laplacian regularized framelet GCNs: Convergence, energy dynamic and as non-linear diffusion
Dai Shi, Zhiqi Shao, Yi Guo, Qibin Zhao, and Junbin Gao · 2024
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Diffusion-based neural network weights generation
Bedionita Soro, Bruno Andreis, Hayeon Lee, Song Chong, Frank Hutter, and Sung Ju Hwang · 2024
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Kai Wang, Zhaopan Xu, Yukun Zhou, Zelin Zang, Trevor Darrell, Zhuang Liu, and Yang You · 2024
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Bregman graph neural network
Jiayu Zhai, Lequan Lin, Dai Shi, and Junbin Gao · 2024
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Metadiff: Meta-learning with conditional diffusion for few-shot learning
Baoquan Zhang, Chuyao Luo, Demin Yu, Xutao Li, Huiwei Lin, Yunming Ye, and Bowen Zhang · 2024
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