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Graph Structure Learning (GSL) has recently garnered considerable attention due to its ability to optimize both the parameters of Graph Neural Networks (GNNs) and the computation graph structure simultaneously.
Structure-activity relationship of mutagenic aromatic and heteroaromatic nitro compounds. correlation with molecular orbital energies and hydrophobicity
Asim Kumar Debnath, Rosa L Lopez de Compadre, Gargi Debnath, Alan J Shusterman, and Corwin Hansch · 1991
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Distinguishing enzyme structures from non-enzymes without alignments
Paul D Dobson and Andrew J Doig · 2003
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Mixing patterns in networks
Mark EJ Newman · 2003
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Protein function prediction via graph kernels
Karsten M Borgwardt, Cheng Soon Ong, Stefan Schönauer, SVN Vishwanathan, Alex J Smola, and Hans-Peter Kriegel · 2005
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Social influence analysis in large-scale networks
Jie Tang, Jimeng Sun, Chi Wang, and Zi Yang · 2009
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Deep graph kernels
Pinar Yanardag and SVN Vishwanathan · 2015
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Revisiting semi-supervised learning with graph embeddings
Zhilin Yang, William Cohen, and Ruslan Salakhudinov · 2016
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A simple yet effective baseline for non-attributed graph classification
Chen Cai and Yusu Wang · 2018
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Predict then propagate: Graph neural networks meet personalized pagerank
Johannes Gasteiger, Aleksandar Bojchevski, and Stephan Günnemann · 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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Graph neural networks for social recommendation
Wenqi Fan, Yao Ma, Qing Li, Yuan He, Eric Zhao, Jiliang Tang, and Dawei Yin · 2019
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Learning discrete structures for graph neural networks
Luca Franceschi, Mathias Niepert, Massimiliano Pontil, and Xiao He · 2019
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Semi-supervised learning with graph learning-convolutional networks
Bo Jiang, Ziyan Zhang, Doudou Lin, Jin Tang, and Bin Luo · 2019
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Relation structure-aware heterogeneous information network embedding
Yuanfu Lu, Chuan Shi, Linmei Hu, and Zhiyuan Liu · 2019
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Pytorch: An imperative style, high-performance deep learning library
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, et al · 2019
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Deep graph library: Towards efficient and scalable deep learning on graphs
Minjie Yu Wang · 2019
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Adversarial examples on graph data: Deep insights into attack and defense
Huijun Wu, Chen Wang, Yuriy Tyshetskiy, Andrew Docherty, Kai Lu, and Liming Zhu · 2019
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Graph transformer networks
Seongjun Yun, Minbyul Jeong, Raehyun Kim, Jaewoo Kang, and Hyunwoo J Kim · 2019
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Hierarchical graph pooling with structure learning
Zhen Zhang, Jiajun Bu, Martin Ester, Jianfeng Zhang, Chengwei Yao, Zhi Yu, and Can Wang · 2019
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Adversarial attacks on graph neural networks via meta learning
Daniel Zügner and Stephan Günnemann · 2019
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Rumor detection on social media with bi-directional graph convolutional networks
Tian Bian, Xi Xiao, Tingyang Xu, Peilin Zhao, Wenbing Huang, Yu Rong, and Junzhou Huang · 2020
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Iterative deep graph learning for graph neural networks: Better and robust node embeddings
Yu Chen, Lingfei Wu, and Mohammed Zaki · 2020
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Exploring structure-adaptive graph learning for robust semi-supervised classification
Xiang Gao, Wei Hu, and Zongming Guo · 2020
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Learning to drop: Robust graph neural network via topological denoising
Dongsheng Luo, Wei Cheng, Wenchao Yu, Bo Zong, Jingchao Ni, Haifeng Chen, and Xiang Zhang · 2021
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Graph structure estimation neural networks
Ruijia Wang, Shuai Mou, Xiao Wang, Wanpeng Xiao, Qi Ju, Chuan Shi, and Xing Xie · 2021
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Speedup robust graph structure learning with low-rank information
Hui Xu, Liyao Xiang, Jiahao Yu, Anqi Cao, and Xinbing Wang · 2021
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Graph-revised convolutional network
Donghan Yu, Ruohong Zhang, Zhengbao Jiang, Yuexin Wu, and Yiming Yang · 2021
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Mining latent structures for multimedia recommendation
Jinghao Zhang, Yanqiao Zhu, Qiang Liu, Shu Wu, Shuhui Wang, and Liang Wang · 2021
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Heterogeneous graph structure learning for graph neural networks
Jianan Zhao, Xiao Wang, Chuan Shi, Binbin Hu, Guojie Song, and Yanfang Ye · 2021
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Asgn: An active semi-supervised graph neural network for molecular property prediction
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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
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Graph structure learning for robust graph neural networks
Wei Jin, Yao Ma, Xiaorui Liu, Xianfeng Tang, Suhang Wang, and Jiliang Tang · 2020
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Towards deeper graph neural networks
Meng Liu, Hongyang Gao, and Shuiwang Ji · 2020
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Tudataset: A collection of benchmark datasets for learning with graphs
Christopher Morris, Nils M Kriege, Franka Bause, Kristian Kersting, Petra Mutzel, and Marion Neumann · 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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Multi-stage self-supervised learning for graph convolutional networks on graphs with few labeled nodes
Ke Sun, Zhouchen Lin, and Zhanxing Zhu · 2020
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A survey on graph structure learning: Progress and opportunities
Yanqiao Zhu, Weizhi Xu, Jinghao Zhang, Yuanqi Du, Jieyu Zhang, Qiang Liu, Carl Yang, and Shu Wu · 2021
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Meta propagation networks for graph few-shot semi-supervised learning
Kaize Ding, Jianling Wang, James Caverlee, and Huan Liu · 2022
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Long range graph benchmark
Vijay Prakash Dwivedi, Ladislav Rampášek, Michael Galkin, Ali Parviz, Guy Wolf, Anh Tuan Luu, and Dominique Beaini · 2022
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Grafn: Semi-supervised node classification on graph with few labels via non-parametric distribution assignment
Junseok Lee, Yunhak Oh, Yeonjun In, Namkyeong Lee, Dongmin Hyun, and Chanyoung Park · 2022
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Compact graph structure learning via mutual information compression
Nian Liu, Xiao Wang, Lingfei Wu, Yu Chen, Xiaojie Guo, and Chuan Shi · 2022
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Towards unsupervised deep graph structure learning
Yixin Liu, Yu Zheng, Daokun Zhang, Hongxu Chen, Hao Peng, and Shirui Pan · 2022
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Graph structure learning with variational information bottleneck
Qingyun Sun, Jianxin Li, Hao Peng, Jia Wu, Xingcheng Fu, Cheng Ji, and S Yu Philip · 2022
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Nodeformer: A scalable graph structure learning transformer for node classification
Qitian Wu, Wentao Zhao, Zenan Li, David P Wipf, and Junchi Yan · 2022
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Evidence-aware fake news detection with graph neural networks
Weizhi Xu, Junfei Wu, Qiang Liu, Shu Wu, and Liang Wang · 2022
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Robust self-supervised structural graph neural network for social network prediction
Yanfu Zhang, Hongchang Gao, Jian Pei, and Heng Huang · 2022
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Homophily-enhanced self-supervision for graph structure learning: Insights and directions
Lirong Wu, Haitao Lin, Zihan Liu, Zicheng Liu, Yufei Huang, and Stan Z Li · 2023
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Self-supervised graph structure refinement for graph neural networks
Jianan Zhao, Qianlong Wen, Mingxuan Ju, Chuxu Zhang, and Yanfang Ye · 2023
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