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Deep learning methods for graphs achieve remarkable performance across a variety of domains.
Automating the construction of internet portals with machine learning
Andrew Kachites McCallum, Kamal Nigam, Jason Rennie, and Kristie Seymore · 2000
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Birds of a feather: Homophily in social networks
Miller McPherson, Lynn Smith-Lovin, and James M Cook · 2001
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Treep: A tree based p2p network architecture
Benoit Hudzia, M-Tahar Kechadi, and Adrian Ottewill · 2005
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Collective classification in network data
Prithviraj Sen, Galileo Namata, Mustafa Bilgic, Lise Getoor, Brian Galligher, and Tina Eliassi-Rad · 2008
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Uncovering biological network function via graphlet degree signatures
Tijana Milenković and Nataša Pržulj · 2008
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A combinatorial approach to graphlet counting
Tomaz Hocevar and Janez Demsar · 2014
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Explaining and harnessing adversarial examples
Ian J Goodfellow, Jonathon Shlens, and Christian Szegedy · 2015
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Semi-supervised classification with graph convolutional networks
Thomas N Kipf and Max Welling · 2017
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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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Adversarial examples for evaluating reading comprehension systems
Robin Jia and Percy Liang · 2017
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Inductive representation learning on large graphs
Will Hamilton, Zhitao Ying, and Jure Leskovec · 2017
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Learning a health knowledge graph from electronic medical records
Maya Rotmensch, Yoni Halpern, Abdulhakim Tlimat, Steven Horng, and David Sontag · 2017
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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 attention networks
Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio, and Yoshua Bengio · 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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Learning structural node embeddings via diffusion wavelets
Claire Donnat, Marinka Zitnik, David Hallac, and Jure Leskovec · 2018
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Boosting adversarial attacks with momentum
Yinpeng Dong, Fangzhou Liao, Tianyu Pang, Hang Su, Jun Zhu, Xiaolin Hu, and Jianguo Li · 2018
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A simple unified framework for detecting out-of-distribution samples and adversarial attacks
Kimin Lee, Kibok Lee, Honglak Lee, and Jinwoo Shin · 2018
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Adversarial attack on graph structured data
Hanjun Dai, Hui Li, Tian Tian, Xin Huang, Lin Wang, Jun Zhu, and Le Song · 2018
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Modeling polypharmacy side effects with graph convolutional networks
Marinka Zitnik, Monica Agrawal, and Jure Leskovec · 2018
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Signed graph convolutional networks
Tyler Derr, Yao Ma, and Jiliang Tang · 2018
Cited alongside, same era.
Large-scale analysis of disease pathways in the human interactome
Monica Agrawal, Marinka Zitnik, and Jure Leskovec · 2018
Cited alongside, same era.
How powerful are graph neural networks?
Keyulu Xu, Weihua Hu, Jure Leskovec, and Stefanie Jegelka · 2019
Cited alongside, same era.
Bringing robustness against adversarial attacks
Gean T Pereira and André CPLF de Carvalho · 2019
Cited alongside, same era.
Adversarial explanations for understanding image classification decisions and improved neural network robustness
Walt Woods, Jack Chen, and Christof Teuscher · 2019
Cited alongside, same era.
Adversarial attacks on medical machine learning
Samuel G Finlayson, John D Bowers, Joichi Ito, Jonathan L Zittrain, Andrew L Beam, and Isaac S Kohane · 2019
Cited alongside, same era.
Fi-gnn: Modeling feature interactions via graph neural networks for ctr prediction
Zekun Li, Zeyu Cui, Shu Wu, Xiaoyu Zhang, and Liang Wang · 2019
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Humans can decipher adversarial images
Zhenglong Zhou and Chaz Firestone · 2019
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Graph embedding on biomedical networks: methods, applications and evaluations
Xiang Yue, Zhen Wang, Jingong Huang, Srinivasan Parthasarathy, Soheil Moosavinasab, Yungui Huang, Simon M Lin, Wen Zhang, Ping Zhang, and Huan Sun · 2020
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Graph embedding and unsupervised learning predict genomic sub-compartments from hic chromatin interaction data
Haitham Ashoor, Xiaowen Chen, Wojciech Rosikiewicz, Jiahui Wang, Albert Cheng, Ping Wang, Yijun Ruan, and Sheng Li · 2020
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Deep learning on graphs: A survey
Ziwei Zhang, Peng Cui, and Wenwu Zhu · 2020
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A comprehensive survey on graph neural networks
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Ai can now defend itself against malicious messages hidden in speech
M Hutson · 2019
Cited alongside, same era.
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
Cited alongside, same era.
Robust graph convolutional networks against adversarial attacks
Dingyuan Zhu, Ziwei Zhang, Peng Cui, and Wenwu Zhu · 2019
Cited alongside, same era.
Label propagation on k-partite graphs with heterophily
Dingxiong Deng, Fan Bai, Yiqi Tang, Shuigeng Zhou, Cyrus Shahabi, et al · 2019
Cited alongside, same era.
Adversarial attacks on graph neural networks via meta learning
Daniel Zügner and Stephan Günnemann · 2019
Cited alongside, same era.
A unified framework for data poisoning attack to graph-based semi-supervised learning
Xuanqing Liu, Si Si, Xiaojin Zhu, Yang Li, and Cho-Jui Hsieh · 2019
Cited alongside, same era.
Zonghan Wu, Shirui Pan, Fengwen Chen, Guodong Long, Chengqi Zhang, and S Yu Philip · 2020
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Deep learning models for electrocardiograms are susceptible to adversarial attack
Xintian Han, Yuxuan Hu, Luca Foschini, Larry Chinitz, Lior Jankelson, and Rajesh Ranganath · 2020
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Model uncertainty, political contestation, and public trust in science: Evidence from the COVID-19 pandemic
SE Kreps and DL Kriner · 2020
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Adversarial attacks and defenses in deep learning
Kui Ren, Tianhang Zheng, Zhan Qin, and Xue Liu · 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
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Transferring robustness for graph neural network against poisoning attacks
Xianfeng Tang, Yandong Li, Yiwei Sun, Huaxiu Yao, Prasenjit Mitra, and Suhang Wang · 2020
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Graphsaint: Graph sampling based inductive learning method
Hanqing Zeng, Hongkuan Zhou, Ajitesh Srivastava, Rajgopal Kannan, and Viktor Prasanna · 2020
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Policy teaching via environment poisoning: Training-time adversarial attacks against reinforcement learning
Amin Rakhsha, Goran Radanovic, Rati Devidze, Xiaojin Zhu, and Adish Singla · 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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Adversarial attacks and defenses on graphs: A review and empirical study
Wei Jin, Yaxin Li, Han Xu, Yiqi Wang, and Jiliang Tang · 2020
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Deciphering interaction fingerprints from protein molecular surfaces using geometric deep learning
Pablo Gainza, Freyr Sverrisson, Frederico Monti, Emanuele Rodola, D Boscaini, MM Bronstein, and BE Correia · 2020
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Graph universal adversarial attacks: A few bad actors ruin graph learning models
Xiao Zang, Yi Xie, Jie Chen, and Bo Yuan · 2020
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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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Deeprobust: A pytorch library for adversarial attacks and defenses
Yaxin Li, Wei Jin, Han Xu, and Jiliang Tang · 2020
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