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As powerful tools for representation learning on graphs, graph neural networks (GNNs) have facilitated various applications from drug discovery to recommender systems.
Social structure of facebook networks
Amanda L Traud, Peter J Mucha, and Mason A Porter · 2012
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Convolutional networks on graphs for learning molecular fingerprints
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Sparsity-aware sensor collaboration for linear coherent estimation
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Domain-adversarial training of neural networks
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Zhilin Yang, William Cohen, and Ruslan Salakhudinov · 2016
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Will Hamilton, Zhitao Ying, and Jure Leskovec · 2017
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Central moment discrepancy (cmd) for domain-invariant representation learning
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Predict then propagate: Graph neural networks meet personalized pagerank
Johannes Klicpera, Aleksandar Bojchevski, and Stephan Günnemann · 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
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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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Adversarial attacks on neural networks for graph data
Daniel Zügner, Amir Akbarnejad, and Stephan Günnemann · 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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Deep graph infomax
Petar Velickovic, William Fedus, William L Hamilton, Pietro Liò, Yoshua Bengio, and R Devon Hjelm · 2019
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A comprehensive survey on graph neural networks
Zonghan Wu, Shirui Pan, Fengwen Chen, Guodong Long, Chengqi Zhang, and Philip S Yu · 2019
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Topology attack and defense for graph neural networks: An optimization perspective
Kaidi Xu, Hongge Chen, Sijia Liu, Pin-Yu Chen, Tsui-Wei Weng, Mingyi Hong, and Xue Lin · 2019
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Robust graph convolutional networks against adversarial attacks
Dingyuan Zhu, Ziwei Zhang, Peng Cui, and Wenwu Zhu · 2019
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Adversarial attacks on graph neural networks via meta learning
Daniel Zügner and Stephan Günnemann · 2019
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A unifying mutual information view of metric learning: cross-entropy vs. pairwise losses
Malik Boudiaf, Jérôme Rony, Imtiaz Masud Ziko, Eric Granger, Marco Pedersoli, Pablo Piantanida, and Ismail Ben Ayed · 2020
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Iterative deep graph learning for graph neural networks: Better and robust node embeddings
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All you need is low (rank) defending against adversarial attacks on graphs
Negin Entezari, Saba A Al-Sayouri, Amirali Darvishzadeh, and Evangelos E Papalexakis · 2020
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Graph random neural networks for semi-supervised learning on graphs
Wenzheng Feng, Jie Zhang, Yuxiao Dong, Yu Han, Huanbo Luan, Qian Xu, Qiang Yang, Evgeny Kharlamov, and Jie Tang · 2020
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Grale: Designing networks for graph learning
Jonathan Halcrow, Alexandru Mosoi, Sam Ruth, and Bryan Perozzi · 2020
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Contrastive multi-view representation learning on graphs
Kaveh Hassani and Amir Hosein Khasahmadi · 2020
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Open graph benchmark: Datasets for machine learning on graphs
Robustness of graph neural networks at scale
Simon Geisler, Tobias Schmidt, Hakan Şirin, Daniel Zügner, Aleksandar Bojchevski, and Stephan Günnemann · 2021
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Adversarial attack on large scale graph
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New benchmarks for learning on non-homophilous graphs
Derek Lim, Xiuyu Li, Felix Hohne, and Ser-Nam Lim · 2021
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Pathfinder discovery networks for neural message passing
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Graph neural networks for friend ranking in large-scale social platforms
Aravind Sankar, Yozen Liu, Jun Yu, and Neil Shah · 2021
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Adversarial graph augmentation to improve graph contrastive learning
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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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Deeprobust: A pytorch library for adversarial attacks and defenses
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Evolvegcn: Evolving graph convolutional networks for dynamic graphs
Aldo Pareja, Giacomo Domeniconi, Jie Chen, Tengfei Ma, Toyotaro Suzumura, Hiroki Kanezashi, Tim Kaler, Tao Schardl, and Charles Leiserson · 2020
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Dropedge: Towards deep graph convolutional networks on node classification
Yu Rong, Wenbing Huang, Tingyang Xu, and Junzhou Huang · 2020
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Adversarial deep network embedding for cross-network node classification
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Self-supervised learning of graph neural networks: A unified review
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Graph contrastive learning automated
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Memo: Test time robustness via adaptation and augmentation
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Sizeshiftreg: a regularization method for improving size-generalization in graph neural networks
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