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Deep graph learning has achieved remarkable progresses in both business and scientific areas ranging from finance and e-commerce, to drug and advanced material discovery.
GraphMix: Improved Training of GNNs for Semi-Supervised Learning
Vikas Verma, Meng Qu, Kenji Kawaguchi, Alex Lamb, Yoshua Bengio, Juho Kannala, and Jian Tang · 1909
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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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The information bottleneck method
Naftali Tishby, Fernando C Pereira, and William Bialek · 2000
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Statistical mechanics of complex networks
Réka Albert and Albert-László Barabási · 2002
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Differential privacy
Cynthia Dwork · 2011
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Recursive deep models for semantic compositionality over a sentiment treebank
Richard Socher, Alex Perelygin, Jean Wu, Jason Chuang, Christopher D Manning, Andrew Y Ng, and Christopher Potts · 2013
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Adaptive recursive neural network for target-dependent twitter sentiment classification
Li Dong, Furu Wei, Chuanqi Tan, Duyu Tang, Ming Zhou, and Ke Xu · 2014
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High-dimensional feature selection by feature-wise kernelized lasso
Makoto Yamada, Wittawat Jitkrittum, Leonid Sigal, Eric P Xing, and Masashi Sugiyama · 2014
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On pixel-wise explanations for non-linear classifier decisions by layer-wise relevance propagation
Sebastian Bach, Alexander Binder, Grégoire Montavon, Frederick Klauschen, Klaus-Robert Müller, and Wojciech Samek · 2015
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Unsupervised domain adaptation by backpropagation
Yaroslav Ganin and Victor S. Lempitsky · 2015
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Semi-supervised classification with graph convolutional networks
Thomas N Kipf and Max Welling · 2016
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Regulation (eu) 2016/679 of the european parliament and of the council of 27 april 2016 on the protection of natural persons with regard to the processing of personal data and on the free movement of such data, and repealing directive 95/46/ec (general data protection regulation)
Eurpean Parliament · 2016
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” why should i trust you?” explaining the predictions of any classifier
Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin · 2016
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Inductive representation learning on large graphs
Will Hamilton, Zhitao Ying, and Jure Leskovec · 2017
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Categorical reparameterization with gumbel-softmax
Eric Jang, Shixiang Gu, and Ben Poole · 2017
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Communication-efficient learning of deep networks from decentralized data
Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, and Blaise Agüera y Arcas · 2017
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Making deep neural networks robust to label noise: A loss correction approach
Giorgio Patrini, Alessandro Rozza, Aditya Krishna Menon, Richard Nock, and Lizhen Qu · 2017
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Sungmin Rhee, Seokjun Seo, and Sun Kim · 2017
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Learning important features through propagating activation differences
Avanti Shrikumar, Peyton Greenside, and Anshul Kundaje · 2017
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Moleculenet: A benchmark for molecular machine learning
Zhenqin Wu, Bharath Ramsundar, Evan N. Feinberg, Joseph Gomes, Caleb Geniesse, Aneesh S. Pappu, Karl Leswing, and Vijay S. Pande · 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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Graph cnn for survival analysis on whole slide pathological images
Ruoyu Li, Jiawen Yao, Xinliang Zhu, Yeqing Li, and Junzhou Huang · 2018
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M-walk: Learning to walk over graphs using monte carlo tree search
Yelong Shen, Jianshu Chen, Po-Sen Huang, Yuqing Guo, and Jianfeng Gao · 2018
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Adversarial attack and defense on graph data: A survey
Lichao Sun, Ji Wang, Philip S. Yu, and Bo Li · 2018
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Split learning for health: Distributed deep learning without sharing raw patient data
Praneeth Vepakomma, Otkrist Gupta, Tristan Swedish, and Ramesh Raskar · 2018
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Moleculenet: a benchmark for molecular machine learning
Zhenqin Wu, Bharath Ramsundar, Evan N Feinberg, Joseph Gomes, Caleb Geniesse, Aneesh S Pappu, Karl Leswing, and Vijay Pande · 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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Martín Arjovsky, Léon Bottou, Ishaan Gulrajani, and David Lopez-Paz · 2019
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Explainability techniques for graph convolutional networks
Federico Baldassarre and Hossein Azizpour · 2019
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Privacy attacks on network embeddings
Michael Ellers, Michael Cochez, Tobias Schumacher, Markus Strohmaier, and Florian Lemmerich · 2019
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Networks in biology
Valeria Fionda · 2019
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Domain-adversarial network alignment
Huiting Hong, Xin Li, Yuangang Pan, and Ivor W. Tsang · 2019
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Latent adversarial training of graph convolution networks
Hongwei Jin and Xinhua Zhang · 2019
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Deepgcns: Can gcns go as deep as cnns?
Guohao Li, Matthias Muller, Ali Thabet, and Bernard Ghanem · 2019
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Semi-supervised graph classification: A hierarchical graph perspective
Jia Li, Yu Rong, Hong Cheng, Helen Meng, Wenbing Huang, and Junzhou Huang · 2019
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Learning graph neural networks with noisy labels
Hoang NT, Choong Jun Jin, and Tsuyoshi Murata · 2019
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Explainability methods for graph convolutional neural networks
Phillip E Pope, Soheil Kolouri, Mohammad Rostami, Charles E Martin, and Heiko Hoffmann · 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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Layerwise relevance visualization in convolutional text graph classifiers
Robert Schwarzenberg, Marc Hübner, David Harbecke, Christoph Alt, and Leonhard Hennig · 2019
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Learning robust representations with graph denoising policy network
Lu Wang, Wenchao Yu, Wei Wang, Wei Cheng, Wei Zhang, Hongyuan Zha, Xiaofeng He, and Haifeng Chen · 2019
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Adversarial examples for 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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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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Federated machine learning: Concept and applications
Qiang Yang, Yang Liu, Tianjian Chen, and Yongxin Tong · 2019
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Gnnexplainer: Generating explanations for graph neural networks
Rex Ying, Dylan Bourgeois, Jiaxuan You, Marinka Zitnik, and Jure Leskovec · 2019
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Data poisoning attack against knowledge graph embedding
Hengtong Zhang, Tianhang Zheng, Jing Gao, Chenglin Miao, Lu Su, Yaliang Li, and Kui Ren · 2019
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Cgc-net: Cell graph convolutional network for grading of colorectal cancer histology images
Yanning Zhou, Simon Graham, Navid Alemi Koohbanani, Muhammad Shaban, Pheng-Ann Heng, and Nasir Rajpoot · 2019
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Representation learning of histopathology images using graph neural networks
Mohammed Adnan, Shivam Kalra, and Hamid R Tizhoosh · 2020
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Histographs: graphs in histopathology
Deepak Anand, Shrey Gadiya, and Amit Sethi · 2020
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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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A restricted black-box adversarial framework towards attacking graph embedding models
Heng Chang, Yu Rong, Tingyang Xu, Wenbing Huang, Honglei Zhang, Peng Cui, Wenwu Zhu, and Junzhou Huang · 2020
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A restricted black-box adversarial framework towards attacking graph embedding models
Heng Chang, Yu Rong, Tingyang Xu, Wenbing Huang, Honglei Zhang, Peng Cui, Wenwu Zhu, and Junzhou Huang · 2020
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Molecule optimization by explainable evolution
Binghong Chen, Tianzhe Wang, Chengtao Li, Hanjun Dai, and Le Song · 2020
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Chaochao Chen, Jamie Cui, Guanfeng Liu, Jia Wu, and Li Wang · 2020
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A survey of adversarial learning on graphs
Liang Chen, Jintang Li, Jiaying Peng, Tao Xie, Zengxu Cao, Kun Xu, Xiangnan He, and Zibin Zheng · 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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Enhancing graph neural network-based fraud detectors against camouflaged fraudsters
Yingtong Dou, Zhiwei Liu, Li Sun, Yutong Deng, Hao Peng, and Philip S. Yu · 2020
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Quantifying privacy leakage in graph embedding
Vasisht Duddu, Antoine Boutet, and Virat Shejwalkar · 2020
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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
Cited alongside, same era.
Explainable subgraph reasoning for forecasting on temporal knowledge graphs
Zhen Han, Peng Chen, Yunpu Ma, and Volker Tresp · 2020
Cited alongside, same era.
Bayesian graph neural networks with adaptive connection sampling
Arman Hasanzadeh, Ehsan Hajiramezanali, Shahin Boluki, Mingyuan Zhou, Nick Duffield, Krishna Narayanan, and Xiaoning Qian · 2020
Cited alongside, same era.
Graphlime: Local interpretable model explanations for graph neural networks
Qiang Huang, Makoto Yamada, Yuan Tian, Dinesh Singh, Dawei Yin, and Yi Chang · 2020
Cited alongside, same era.
Towards explainable graph representations in digital pathology
Guillaume Jaume, Pushpak Pati, Antonio Foncubierta-Rodriguez, Florinda Feroce, Giosue Scognamiglio, Anna Maria Anniciello, Jean-Philippe Thiran, Orcun Goksel, and Maria Gabrani · 2020
Cf-gnnexplainer: Counterfactual explanations for graph neural networks
Ana Lucic, Maartje ter Hoeve, Gabriele Tolomei, Maarten de Rijke, and Fabrizio Silvestri · 2021
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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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Molecular graph enhanced transformer for retrosynthesis prediction
Kelong Mao, Xi Xiao, Tingyang Xu, Yu Rong, Junzhou Huang, and Peilin Zhao · 2021
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Meg: Generating molecular counterfactual explanations for deep graph networks
Danilo Numeroso and Davide Bacciu · 2021
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Membership inference attack on graph neural networks
Iyiola E. Olatunji, Wolfgang Nejdl, and Megha Khosla · 2021
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Cited alongside, same era.
Adversarial attacks and defenses on graphs: A review and empirical study
Wei Jin, Yaxin Li, Han Xu, Yiqi Wang, and Jiliang Tang · 2020
Cited alongside, same era.
Graph structure learning for robust graph neural networks
Wei Jin, Yao Ma, Xiaorui Liu, Xianfeng Tang, Suhang Wang, and Jiliang Tang · 2020
Cited alongside, same era.
Multi-objective molecule generation using interpretable substructures
Wengong Jin et al · 2020
Cited alongside, same era.
FLAG: adversarial data augmentation for graph neural networks
Kezhi Kong, Guohao Li, Mucong Ding, Zuxuan Wu, Chen Zhu, Bernard Ghanem, Gavin Taylor, and Tom Goldstein · 2020
Cited alongside, same era.
Parameterized explainer for graph neural network
Dongsheng Luo, Wei Cheng, Dongkuan Xu, Wenchao Yu, Bo Zong, Haifeng Chen, and Xiang Zhang · 2020
Cited alongside, same era.
Deep graph learning with property augmentation for predicting drug-induced liver injury
Hehuan Ma, Weizhi An, Yuhong Wang, Hongmao Sun, Ruili Huang, and Junzhou Huang · 2020
Cited alongside, same era.
Learning reasoning strategies in end-to-end differentiable proving
Pasquale Minervini, Sebastian Riedel, Pontus Stenetorp, Edward Grefenstette, and Tim Rocktäschel · 2020
Cited alongside, same era.
Later among the works it cites.
Dropgnn: random dropouts increase the expressiveness of graph neural networks
Pál András Papp, Karolis Martinkus, Lukas Faber, and Roger Wattenhofer · 2021
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On the connections between counterfactual explanations and adversarial examples
Martin Pawelczyk, Shalmali Joshi, Chirag Agarwal, Sohini Upadhyay, and Himabindu Lakkaraju · 2021
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Differentially private federated knowledge graphs embedding
Hao Peng, Haoran Li, Yangqiu Song, Vincent Zheng, and Jianxin Li · 2021
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Quantitative evaluation of explainable graph neural networks for molecular property prediction
Jiahua Rao, Shuangjia Zheng, and Yuedong Yang · 2021
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Locally private graph neural networks
Sina Sajadmanesh and Daniel Gatica-Perez · 2021
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Interpreting graph neural networks for nlp with differentiable edge masking
Michael Sejr Schlichtkrull, Nicola De Cao, and Ivan Titov · 2021
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Reinforcement learning enhanced explainer for graph neural networks
Caihua Shan, Yifei Shen, Yao Zhang, Xiang Li, and Dongsheng Li · 2021
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Classification of intestinal gland cell-graphs using graph neural networks
Linda Studer, Jannis Wallau, Heather Dawson, Inti Zlobec, and Andreas Fischer · 2021
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Generalizing to unseen domains: A survey on domain generalization
Jindong Wang, Cuiling Lan, Chang Liu, Yidong Ouyang, and Tao Qin · 2021
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Towards multi-grained explainability for graph neural networks
Xiang Wang, Yingxin Wu, An Zhang, Xiangnan He, and Tat-Seng Chua · 2021
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Be confident! towards trustworthy graph neural networks via confidence calibration
Xiao Wang, Hongrui Liu, Chuan Shi, and Cheng Yang · 2021
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Mixup for node and graph classification
Yiwei Wang, Wei Wang, Yuxuan Liang, Yujun Cai, and Bryan Hooi · 2021
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Membership inference attacks on knowledge graphs
Yu Wang and Lichao Sun · 2021
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Fedgnn: Federated graph neural network for privacy-preserving recommendation
Chuhan Wu, Fangzhao Wu, Yang Cao, Yongfeng Huang, and Xing Xie · 2021
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Lirong Wu, Haitao Lin, Zhangyang Gao, Cheng Tan, Stan Li, et al · 2021
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Lirong Wu, Haitao Lin, Zhangyang Gao, Cheng Tan, and Stan Z. Li · 2021
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Graph backdoor
Zhaohan Xi, Ren Pang, Shouling Ji, and Ting Wang · 2021
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Federated graph classification over non-iid graphs
Han Xie, Jing Ma, Li Xiong, and Carl Yang · 2021
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Robustness of deep learning models on graphs: A survey
Jiarong Xu, Junru Chen, Siqi You, Zhiqing Xiao, Yang Yang, and Jiangang Lu · 2021
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Explainability-based backdoor attacks against graph neural networks
Jing Xu, Minhui Xue, and Stjepan Picek · 2021
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Legalgnn: Legal information enhanced graph neural network for recommendation
Jun Yang, Weizhi Ma, Min Zhang, Xin Zhou, Yiqun Liu, and Shaoping Ma · 2021
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Towards the explanation of graph neural networks in digital pathology with information flows
Junchi Yu, Tingyang Xu, and Ran He · 2021
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Graph information bottleneck for subgraph recognition
Junchi Yu, Tingyang Xu, Yu Rong, Yatao Bian, Junzhou Huang, and Ran He · 2021
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Recognizing predictive substructures with subgraph information bottleneck
Junchi Yu, Tingyang Xu, Yu Rong, Yatao Bian, Junzhou Huang, and Ran He · 2021
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On explainability of graph neural networks via subgraph explorations
Hao Yuan, Haiyang Yu, Jie Wang, Kang Li, and Shuiwang Ji · 2021
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Federated graph learning - A position paper
Huanding Zhang, Tao Shen, Fei Wu, Mingyang Yin, Hongxia Yang, and Chao Wu · 2021
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Relex: A model-agnostic relational model explainer
Yue Zhang, David Defazio, and Arti Ramesh · 2021
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Backdoor attacks to graph neural networks
Zaixi Zhang, Jinyuan Jia, Binghui Wang, and Neil Zhenqiang Gong · 2021
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Graphmi: Extracting private graph data from graph neural networks
Zaixi Zhang, Qi Liu, Zhenya Huang, Hao Wang, Chengqiang Lu, Chuanren Liu, and Enhong Chen · 2021
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A comparative study on robust graph neural networks to structural noises
Zeyu Zhang and Yulong Pei · 2021
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Inference attacks against graph neural networks
Zhikun Zhang, Min Chen, Michael Backes, Yun Shen, and Yang Zhang · 2021
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Expressive 1-lipschitz neural networks for robust multiple graph learning against adversarial attacks
Xin Zhao, Zeru Zhang, Zijie Zhang, Lingfei Wu, Jiayin Jin, Yang Zhou, Ruoming Jin, Dejing Dou, and Da Yan · 2021
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ASFGNN: automated separated-federated graph neural network
Longfei Zheng, Jun Zhou, Chaochao Chen, Bingzhe Wu, Li Wang, and Benyu Zhang · 2021
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Domain generalization in vision: A survey
Kaiyang Zhou, Ziwei Liu, Yu Qiao, Tao Xiang, and Chen Change Loy · 2021
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Deep graph structure learning for robust representations: A survey
Yanqiao Zhu, Weizhi Xu, Jinghao Zhang, Qiang Liu, Shu Wu, and Liang Wang · 2021
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TDGIA: effective injection attacks on graph neural networks
Xu Zou, Qinkai Zheng, Yuxiao Dong, Xinyu Guan, Evgeny Kharlamov, Jialiang Lu, and Jie Tang · 2021
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Adversarial attack framework on graph embedding models with limited knowledge
Heng Chang, Yu Rong, Tingyang Xu, Wenbing Huang, Honglei Zhang, Peng Cui, Xin Wang, Wenwu Zhu, and Junzhou Huang · 2022
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Understanding and improving graph injection attack by promoting unnoticeability
Yongqiang Chen, Han Yang, Yonggang Zhang, MA KAILI, Tongliang Liu, Bo Han, and James Cheng · 2022
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Financial time series forecasting with multi-modality graph neural network
Dawei Cheng, Fangzhou Yang, Sheng Xiang, and Jin Liu · 2022
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Decomposition based explanation for graph neural networks
Qizhang Feng, Ninghao Liu, Fan Yang, Ruixiang Tang, Mengnan Du, and Hu Xia · 2022
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DrugOOD: Out-of-Distribution (OOD) Dataset Curator and Benchmark for AI-aided Drug Discovery – A Focus on Affinity Prediction Problems with Noise Annotations
Yuanfeng Ji, Lu Zhang, Jiaxiang Wu, Bingzhe Wu, Long-Kai Huang, Tingyang Xu, Yu Rong, Lanqing Li, Jie Ren, Ding Xue, Houtim Lai, Shaoyong Xu, Jing Feng, Wei Liu, Ping Luo, Shuigeng Zhou, Junzhou Huang, Peilin Zhao, and Yatao Bian · 2022
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Fair node representation learning via adaptive data augmentation, 2022
Oyku Deniz Kose and Yanning Shen · 2022
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Guard: Graph universal adversarial defense
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Explainability in graph neural networks: An experimental survey
Peibo Li, Yixing Yang, Maurice Pagnucco, and Yang Song · 2022
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Cross-dependent graph neural networks for molecular property prediction
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Masked transformer for neighhourhood-aware click-through rate prediction
Erxue Min, Yu Rong, Tingyang Xu, Yatao Bian, Peilin Zhao, Junzhou Huang, Da Luo, Kangyi Lin, and Sophia Ananiadou · 2022
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Towards distribution shift of node-level prediction on graphs: An invariance perspective
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Discovering invariant rationales for graph neural networks
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Improving subgraph recognition with variational graph information bottleneck
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Structure-aware conditional variational auto-encoder for constrained molecule optimization
Junchi Yu, Tingyang Xu, Yu Rong, Junzhou Huang, and Ran He · 2022
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Unsupervised graph poisoning attack via contrastive loss back-propagation
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Fairness amidst non-iid graph data: A literature review
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