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Graph neural networks (GNNs) have recently gained much attention for node and graph classification tasks on graph-structured data.
IX. On the problem of the most efficient tests of statistical hypotheses
Jerzy Neyman and Egon Sharpe Pearson. 1933 · 1933
Earlier work this paper cites.
Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference
Judea Pearl. 1988 · 1988
Earlier work this paper cites.
Semi-supervised learning using gaussian fields and harmonic functions. In ICML
Xiaojin Zhu, Zoubin Ghahramani, and John D Lafferty. 2003 · 2003
Earlier work this paper cites.
Testing statistical hypotheses
Erich L Lehmann and Joseph P Romano. 2006 · 2006
Earlier work this paper cites.
Netprobe: a fast and scalable system for fraud detection in online auction networks. In WWW
Shashank Pandit, Horng Chau, Samuel Wang, and Christos Faloutsos. 2007 · 2007
Earlier work this paper cites.
Collective classification in network data
Prithviraj Sen, Galileo Namata, Mustafa Bilgic, and et al. 2008 · 2008
Earlier work this paper cites.
Certifiable Robustness of Graph Convolutional Networks under Structure Perturbations. In KDD
Daniel Zügner and Stephan Günnemann. 2020 · 2008
Earlier work this paper cites.
To join or not to join: the illusion of privacy in social networks with mixed public and private user profiles. In WWW
Elena Zheleva and Lise Getoor. 2009 · 2009
Earlier work this paper cites.
You are who you know: inferring user profiles in online social networks. In WSDM
Alan Mislove, Bimal Viswanath, Krishna P Gummadi, and Peter Druschel. 2010 · 2010
Earlier work this paper cites.
Sybilbelief: A semi-supervised learning approach for structure-based sybil detection
Neil Zhenqiang Gong, Mario Frank, and Prateek Mittal. 2014 · 2014
Earlier work this paper cites.
Guilt by association: large scale malware detection by mining file-relation graphs. In KDD
Acar Tamersoy, Kevin Roundy, and Duen Horng Chau. 2014 · 2014
Earlier work this paper cites.
Towards Verification of Artificial Neural Networks.. In MBMV
Karsten Scheibler, Leonore Winterer, Ralf Wimmer, and Bernd Becker. 2015 · 2015
Earlier work this paper cites.
Deep graph kernels. In KDD
Pinar Yanardag and SVN Vishwanathan. 2015 · 2015
Earlier work this paper cites.
You are who you know and how you behave: Attribute inference attacks via users’ social friends and behaviors. In { \{ USENIX } \} Security Symposium
Neil Zhenqiang Gong and Bin Liu. 2016 · 2016
Earlier work this paper cites.
Mitigating evasion attacks to deep neural networks via region-based classification. In ACSAC
Xiaoyu Cao and Neil Zhenqiang Gong. 2017 · 2017
Earlier work this paper cites.
Provably minimally-distorted adversarial examples
Nicholas Carlini, Guy Katz, Clark Barrett, and David L Dill. 2017 · 2017
Earlier work this paper cites.
Maximum resilience of artificial neural networks. In ATVA
Chih-Hong Cheng, Georg Nührenberg, and Harald Ruess. 2017 · 2017
Earlier work this paper cites.
Formal verification of piece-wise linear feed-forward neural networks. In ATVA
Ruediger Ehlers. 2017 · 2017
Earlier work this paper cites.
Neural message passing for quantum chemistry. In ICML
Justin Gilmer, Samuel S Schoenholz, Patrick F Riley, Oriol Vinyals, and George E Dahl. 2017 · 2017
Earlier work this paper cites.
Inductive representation learning on large graphs. In NIPS
Will Hamilton, Zhitao Ying, and Jure Leskovec. 2017 · 2017
Earlier work this paper cites.
AttriInfer: Inferring user attributes in online social networks using markov random fields. In WWW
Jinyuan Jia, Binghui Wang, Le Zhang, and Neil Zhenqiang Gong. 2017 · 2017
Cited alongside, same era.
Reluplex: An efficient SMT solver for verifying deep neural networks. In CAV
Guy Katz, Clark Barrett, David L Dill, and et al. 2017 · 2017
Cited alongside, same era.
Semi-supervised classification with graph convolutional networks. In ICLR
Thomas N Kipf and Max Welling. 2017 · 2017
Cited alongside, same era.
A unified view of piecewise linear neural network verification. In NeurIPS
Rudy R Bunel, Ilker Turkaslan, Philip Torr, Pushmeet Kohli, and Pawan K Mudigonda. 2018 · 2018
Cited alongside, same era.
Adversarial attack on graph structured data. In ICML
Hanjun Dai, Hui Li, Tian Tian, Xin Huang, Lin Wang, Jun Zhu, and Le Song. 2018 · 2018
Cited alongside, same era.
Training verified learners with learned verifiers
Predict then propagate: Graph neural networks meet pagerank. In ICLR
Johannes Klicpera, Aleksandar Bojchevski, and Stephan Günnemann. 2019 · 2019
Later among the works it cites.
Certified robustness to adversarial examples with differential privacy. In IEEE S & P
Mathias Lecuyer, Vaggelis Atlidakis, Roxana Geambasu, Daniel Hsu, and Suman Jana. 2019 · 2019
Later among the works it cites.
Tight Certificates of Adversarial Robustness for Randomly Smoothed Classifiers. In NeurIPS
GuangHe Lee, Yang Yuan, Shiyu Chang, and Tommi Jaakkola. 2019 · 2019
Later among the works it cites.
Certified Adversarial Robustness with Additive Noise
Bai Li, Changyou Chen, Wenlin Wang, and Lawrence Carin. 2019 · 2019
Later among the works it cites.
Provably robust deep learning via adversarially trained smoothed classifiers. In NeurIPS
Hadi Salman, Jerry Li, Ilya Razenshteyn, Pengchuan Zhang, Huan Zhang, Sebastien Bubeck, and Greg Yang. 2019 · 2019
Later among the works it cites.
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Krishnamurthy Dvijotham, Sven Gowal, Robert Stanforth, and et al. 2018a · 2018
Cited alongside, same era.
Deep neural networks and mixed integer linear optimization
Matteo Fischetti and Jason Jo. 2018 · 2018
Cited alongside, same era.
Ai2: Safety and robustness certification of neural networks with abstract interpretation. In IEEE S & P
Timon Gehr, Matthew Mirman, Dana Drachsler-Cohen, Petar Tsankov, Swarat Chaudhuri, and Martin Vechev. 2018 · 2018
Cited alongside, same era.
Towards robust neural networks via random self-ensemble. In ECCV
Xuanqing Liu, Minhao Cheng, Huan Zhang, and Cho-Jui Hsieh. 2018 · 2018
Cited alongside, same era.
Differentiable abstract interpretation for provably robust neural networks. In ICML
Matthew Mirman, Timon Gehr, and Martin Vechev. 2018 · 2018
Cited alongside, same era.
Fast and effective robustness certification. In NeurIPS
Gagandeep Singh, Timon Gehr, Matthew Mirman, Markus Püschel, and Martin Vechev. 2018 · 2018
Cited alongside, same era.
Graph attention networks. In ICLR
Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio, and Yoshua Bengio. 2018 · 2018
Cited alongside, same era.
Attacking Graph-based Classification via Manipulating the Graph Structure. In CCS
Binghui Wang and Neil Zhenqiang Gong. 2019 · 2019
Later among the works it cites.
Graph-based security and privacy analytics via collective classification with joint weight learning and propagation. In NDSS
Binghui Wang, Jinyuan Jia, and Neil Zhenqiang Gong. 2019 · 2019
Later among the works it cites.
Anti-Money Laundering in Bitcoin: Experimenting with Graph Convolutional Networks for Financial Forensics. In KDD Workshop
Mark Weber, Giacomo Domeniconi, Jie Chen, and et al. 2019 · 2019
Later among the works it cites.
Adversarial Examples on Graph Data: Deep Insights into Attack and Defense. In IJCAI
Huijun Wu, Chen Wang, Yuriy Tyshetskiy, Andrew Docherty, Kai Lu, and Liming Zhu. 2019 · 2019
Later among the works it cites.
Robust Graph Convolutional Networks Against Adversarial Attacks. In KDD
Dingyuan Zhu, Ziwei Zhang, Peng Cui, and Wenwu Zhu. 2019 · 2019
Later among the works it cites.
Efficient robustness certificates for discrete data: Sparsity-aware randomized smoothing for graphs, images and more. In ICML
Aleksandar Bojchevski, Johannes Klicpera, and Stephan Günnemann. 2020 · 2020
Closest in time.
A restricted black-box adversarial framework towards attacking graph embedding models. In AAAI
Heng Chang, Yu Rong, Tingyang Xu, Wenbing Huang, Honglei Zhang, Peng Cui, Wenwu Zhu, and Junzhou Huang. 2020 · 2020
Closest in time.
All You Need Is Low (Rank) Defending Against Adversarial Attacks on Graphs. In WSDM
Negin Entezari, Saba A Al-Sayouri, Amirali Darvishzadeh, and Evangelos E Papalexakis. 2020 · 2020
Closest in time.
Certified Robustness of Graph Convolution Networks for Graph Classification under Topological Attacks. In NeurIPS
Hongwei Jin, Zhan Shi, Venkata Jaya Shankar Ashish Peruri, and Xinhua Zhang. 2020 · 2020
Closest in time.
Robustness Certificates for Sparse Adversarial Attacks by Randomized Ablation. In AAAI
Alexander Levine and Soheil Feizi. 2020 · 2020
Closest in time.
Adversarial Attacks on Graph Neural Networks via Node Injections: A Hierarchical Reinforcement Learning Approach. In The Web Conference
Yiwei Sun, Suhang Wang, Xianfeng Tang, Tsung-Yu Hsieh, and Vasant Honavar. 2020 · 2020
Closest in time.
Transferring Robustness for Graph Neural Network Against Poisoning Attacks. In WSDM
Xianfeng Tang, Yandong Li, Yiwei Sun, Huaxiu Yao, Prasenjit Mitra, and Suhang Wang. 2020 · 2020
Closest in time.
MACER: Attack-free and Scalable Robust Training via Maximizing Certified Radius. In ICLR
Runtian Zhai, Chen Dan, Di He, Huan Zhang, Boqing Gong, Pradeep Ravikumar, Cho-Jui Hsieh, and Liwei Wang. 2020 · 2020
Closest in time.
Adversarial Immunization for Certifiable Robustness on Graphs. In WSDM
Shuchang Tao, Huawei Shen, Qi Cao, Liang Hou, and Xueqi Cheng. 2021 · 2021
Closest in time.