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Despite Graph Neural Networks demonstrating considerable promise in graph representation learning tasks, GNNs predominantly face significant issues with over-fitting and over-smoothing as they go deeper as models of computer vision realm.
Cluster-gcn: An efficient algorithm for training deep and large graph convolutional networks
Wei-Lin Chiang, Xuanqing Liu, Si Si, Yang Li, Samy Bengio, and Cho-Jui Hsieh · 1905
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Spectral networks and deep locally connected networks on graphs
Joan Bruna Estrach, Wojciech Zaremba, Arthur Szlam, and Yann LeCun · 2014
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Neural message passing for quantum chemistry
Justin Gilmer, Samuel S Schoenholz, Patrick F Riley, Oriol Vinyals, and George E Dahl · 2017
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Inductive representation learning on large graphs
Will Hamilton, Zhitao Ying, and Jure Leskovec · 2017
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Densely connected convolutional networks
Gao Huang, Zhuang Liu, Laurens Van Der Maaten, and Kilian Q Weinberger · 2017
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Semi-Supervised Classification with Graph Convolutional Networks
Thomas N. Kipf and Max Welling · 2017
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Graph attention networks
Petar Velickovic, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio, and Yoshua Bengio · 2017
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Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio, and Yoshua Bengio · 2017
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Fastgcn: fast learning with graph convolutional networks via importance sampling
Jie Chen, Tengfei Ma, and Cao Xiao · 2018
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Provable and practical approximations for the degree distribution using sublinear graph samples
Talya Eden, Shweta Jain, Ali Pinar, Dana Ron, and C Seshadhri · 2018
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The lottery ticket hypothesis: Finding sparse, trainable neural networks
Jonathan Frankle and Michael Carbin · 2018
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Deeper insights into graph convolutional networks for semi-supervised learning
Qimai Li, Zhichao Han, and Xiao-Ming Wu · 2018
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Representation learning on graphs with jumping knowledge networks
Keyulu Xu, Chengtao Li, Yonglong Tian, Tomohiro Sonobe, Ken-ichi Kawarabayashi, and Stefanie Jegelka · 2018
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Hierarchical graph representation learning with differentiable pooling
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Link prediction based on graph neural networks
Muhan Zhang and Yixin Chen · 2018
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Graph u-nets
Hongyang Gao and Shuiwang Ji · 2019
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Diffusion improves graph learning
Johannes Gasteiger, Stefan Weißenberger, and Stephan Günnemann · 2019
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Self-attention graph pooling
Junhyun Lee, Inyeop Lee, and Jaewoo Kang · 2019
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Deepgcns: Can gcns go as deep as cnns?
Guohao Li, Matthias Müller, Ali Thabet, and Bernard Ghanem · 2019
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Dropedge: Towards deep graph convolutional networks on node classification
Yu Rong, Wenbing Huang, Tingyang Xu, and Junzhou Huang · 2019
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Adagcn: Adaboosting graph convolutional networks into deep models
Ke Sun, Zhanxing Zhu, and Zhouchen Lin · 2019
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Simplifying graph convolutional networks
Felix Wu, Amauri Souza, Tianyi Zhang, Christopher Fifty, Tao Yu, and Kilian Weinberger · 2019
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How powerful are graph neural networks?
Proving the lottery ticket hypothesis: Pruning is all you need
Eran Malach, Gilad Yehudai, Shai Shalev-Schwartz, and Ohad Shamir · 2020
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Costas Mavromatis and George Karypis · 2020
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Scattering gcn: Overcoming oversmoothness in graph convolutional networks
Yimeng Min, Frederik Wenkel, and Guy Wolf · 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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Asap: Adaptive structure aware pooling for learning hierarchical graph representations
Ekagra Ranjan, Soumya Sanyal, and Partha Talukdar · 2020
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Keyulu Xu, Weihua Hu, Jure Leskovec, and Stefanie Jegelka · 2019
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Inductive matrix completion based on graph neural networks
Muhan Zhang and Yixin Chen · 2019
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Pairnorm: Tackling oversmoothing in gnns
Lingxiao Zhao and Leman Akoglu · 2019
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Layer-dependent importance sampling for training deep and large graph convolutional networks
Difan Zou, Ziniu Hu, Yewen Wang, Song Jiang, Yizhou Sun, and Quanquan Gu · 2019
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N-gcn: Multi-scale graph convolution for semi-supervised node classification
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What is the state of neural network pruning?
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Adaptive universal generalized pagerank graph neural network
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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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A unified lottery ticket hypothesis for graph neural networks
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Audio lottery: Speech recognition made ultra-lightweight, noise-robust, and transferable
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Deepgcns: Making gcns go as deep as cnns
Guohao Li, Matthias Müller, Guocheng Qian, Itzel Carolina Delgadillo Perez, Abdulellah Abualshour, Ali Kassem Thabet, and Bernard Ghanem · 2021
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Sanity checks for lottery tickets: Does your winning ticket really win the jackpot?
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Structure-aware hierarchical graph pooling using information bottleneck
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Optimization of graph neural networks: Implicit acceleration by skip connections and more depth
Keyulu Xu, Mozhi Zhang, Stefanie Jegelka, and Kenji Kawaguchi · 2021
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Gebt: Drawing early-bird tickets in graph convolutional network training
Haoran You, Zhihan Lu, Zijian Zhou, and Yingyan Lin · 2021
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Hierarchical multi-view graph pooling with structure learning
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Dual lottery ticket hypothesis
Yue Bai, Huan Wang, Zhiqiang Tao, Kunpeng Li, and Yun Fu · 2022
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Searching lottery tickets in graph neural networks: A dual perspective
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Structural entropy guided graph hierarchical pooling
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Pre-train, prompt, and predict: A systematic survey of prompting methods in natural language processing
Pengfei Liu, Weizhe Yuan, Jinlan Fu, Zhengbao Jiang, Hiroaki Hayashi, and Graham Neubig · 2023
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