Fetching the paper…
Reading the bibliography…
Graph Neural Networks (GNNs) have achieved promising performance in various real-world applications.
An approximate distribution of estimates of variance components
Franklin E Satterthwaite · 1946
Earlier work this paper cites.
The generalization of ‘student’s’problem when several different population varlances are involved
Bernard L Welch · 1947
Earlier work this paper cites.
Random graphs
E. N. Gilbert · 1959
Earlier work this paper cites.
An analysis of variance test for normality (complete samples)
Samuel Sanford Shapiro and Martin B Wilk · 1965
Earlier work this paper cites.
Watermarking digital image and video data. a state-of-the-art overview
Gerhard C Langelaar, Iwan Setyawan, and Reginald L Lagendijk · 2000
Earlier work this paper cites.
Collective classification in network data
Prithviraj Sen, Galileo Namata, Mustafa Bilgic, Lise Getoor, Brian Galligher, and Tina Eliassi-Rad · 2008
Earlier work this paper cites.
Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups
Geoffrey Hinton, Li Deng, Dong Yu, George E Dahl, Abdel-rahman Mohamed, Navdeep Jaitly, Andrew Senior, Vincent Vanhoucke, Patrick Nguyen, Tara N Sainath, et al · 2012
Earlier work this paper cites.
Subgraph matching kernels for attributed graphs
Nils Kriege and Petra Mutzel · 2012
Earlier work this paper cites.
Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton · 2012
Earlier work this paper cites.
Speech recognition with deep recurrent neural networks
Alex Graves, Abdel-rahman Mohamed, and Geoffrey Hinton · 2013
Earlier work this paper cites.
Very deep convolutional networks for large-scale image recognition
Karen Simonyan and Andrew Zisserman · 2014
Earlier work this paper cites.
How transferable are features in deep neural networks?
Jason Yosinski, Jeff Clune, Yoshua Bengio, and Hod Lipson · 2014
Earlier work this paper cites.
Deep graph kernels
Pinar Yanardag and SVN Vishwanathan · 2015
Earlier work this paper cites.
A comparative study of inductive and transductive learning with feedforward neural networks
Monica Bianchini, Anas Belahcen, and Franco Scarselli · 2016
Earlier work this paper cites.
A primer on neural network models for natural language processing
Yoav Goldberg · 2016
Earlier work this paper cites.
Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
Earlier work this paper cites.
Stealing machine learning models via prediction { \{ APIs } \}
Florian Tramèr, Fan Zhang, Ari Juels, Michael K Reiter, and Thomas Ristenpart · 2016
Earlier work this paper cites.
The asa statement on p-values: context, process, and purpose, 2016
Ronald L Wasserstein and Nicole A Lazar · 2016
Earlier work this paper cites.
Inductive representation learning on large graphs
Will Hamilton, Zhitao Ying, and Jure Leskovec · 2017
Earlier work this paper cites.
Semi-supervised classification with graph convolutional networks
Thomas N. Kipf and Max Welling · 2017
Earlier work this paper cites.
Trojaning attack on neural networks
Yingqi Liu, Shiqing Ma, Yousra Aafer, Wen-Chuan Lee, Juan Zhai, Weihang Wang, and Xiangyu Zhang · 2017
Cited alongside, same era.
Practical black-box attacks against machine learning
Nicolas Papernot, Patrick McDaniel, Ian Goodfellow, Somesh Jha, Z Berkay Celik, and Ananthram Swami · 2017
Cited alongside, same era.
Embedding watermarks into deep neural networks
Yusuke Uchida, Yuki Nagai, Shigeyuki Sakazawa, and Shin’ichi Satoh · 2017
Cited alongside, same era.
Turning your weakness into a strength: Watermarking deep neural networks by backdooring
Yossi Adi, Carsten Baum, Moustapha Cisse, Benny Pinkas, and Joseph Keshet · 2018
Cited alongside, same era.
Detecting backdoor attacks on deep neural networks by activation clustering
Bryant Chen, Wilka Carvalho, Nathalie Baracaldo, Heiko Ludwig, Benjamin Edwards, Taesung Lee, Ian Molloy, and Biplav Srivastava · 2018
Cited alongside, same era.
Mark Weber, Giacomo Domeniconi, Jie Chen, Daniel Karl I Weidele, Claudio Bellei, Tom Robinson, and Charles E Leiserson · 2019
Later among the works it cites.
Februus: Input purification defense against trojan attacks on deep neural network systems
Bao Gia Doan, Ehsan Abbasnejad, and Damith C Ranasinghe · 2020
Later among the works it cites.
Invisible backdoor attacks on deep neural networks via steganography and regularization
Shaofeng Li, Minhui Xue, Benjamin Zi Hao Zhao, Haojin Zhu, and Xinpeng Zhang · 2020
Later among the works it cites.
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
Later among the works it cites.
Trojanzoo: Everything you ever wanted to know about neural backdoors (but were afraid to ask)
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Fine-pruning: Defending against backdooring attacks on deep neural networks
Kang Liu, Brendan Dolan-Gavitt, and Siddharth Garg · 2018
Cited alongside, same era.
Inductive–transductive learning with graph neural networks
Alberto Rossi, Matteo Tiezzi, Giovanna Maria Dimitri, Monica Bianchini, Marco Maggini, and Franco Scarselli · 2018
Cited alongside, same era.
Graph Attention Networks
Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Liò, and Yoshua Bengio · 2018
Cited alongside, same era.
How powerful are graph neural networks?
Keyulu Xu, Weihua Hu, Jure Leskovec, and Stefanie Jegelka · 2018
Cited alongside, same era.
Hierarchical graph representation learning with differentiable pooling
Zhitao Ying, Jiaxuan You, Christopher Morris, Xiang Ren, Will Hamilton, and Jure Leskovec · 2018
Cited alongside, same era.
Protecting intellectual property of deep neural networks with watermarking
Jialong Zhang, Zhongshu Gu, Jiyong Jang, Hui Wu, Marc Ph Stoecklin, Heqing Huang, and Ian Molloy · 2018
Cited alongside, same era.
An end-to-end deep learning architecture for graph classification
Muhan Zhang, Zhicheng Cui, Marion Neumann, and Yixin Chen · 2018
Cited alongside, same era.
Ren Pang, Zheng Zhang, Xiangshan Gao, Zhaohan Xi, Shouling Ji, Peng Cheng, and Ting Wang · 2020
Later among the works it cites.
A systematic review on model watermarking for neural networks
Franziska Boenisch · 2021
Closest in time.
A systematic review on model watermarking for neural networks
Franziska Boenisch · 2021
Closest in time.
Graph-free knowledge distillation for graph neural networks
Xiang Deng and Zhongfei Zhang · 2021
Closest in time.
Knowledge distillation: A survey
Jianping Gou, Baosheng Yu, Stephen J Maybank, and Dacheng Tao · 2021
Closest in time.
A survey of deep neural network watermarking techniques
Yue Li, Hongxia Wang, and Mauro Barni · 2021
Closest in time.
Does knowledge distillation really work?
Samuel Stanton, Pavel Izmailov, Polina Kirichenko, Alexander A Alemi, and Andrew G Wilson · 2021
Closest in time.
Data-free model extraction
Jean-Baptiste Truong, Pratyush Maini, Robert J Walls, and Nicolas Papernot · 2021
Closest in time.
A comprehensive survey on graph neural networks
Zonghan Wu, Shirui Pan, Fengwen Chen, Guodong Long, Chengqi Zhang, and Philip S. Yu · 2021
Closest in time.
Graph backdoor
Zhaohan Xi, Ren Pang, Shouling Ji, and Ting Wang · 2021
Closest in time.
Explainability-based backdoor attacks against graph neural networks
Jing Xu, Minhui Xue, and Stjepan Picek · 2021
Closest in time.
Detecting ai trojans using meta neural analysis
Xiaojun Xu, Qi Wang, Huichen Li, Nikita Borisov, Carl A Gunter, and Bo Li · 2021
Closest in time.
Backdoor attacks to graph neural networks
Zaixi Zhang, Jinyuan Jia, Binghui Wang, and Neil Zhenqiang Gong · 2021
Closest in time.
Red alarm for pre-trained models: Universal vulnerability to neuron-level backdoor attacks
Zhengyan Zhang, Guangxuan Xiao, Yongwei Li, Tian Lv, Fanchao Qi, Zhiyuan Liu, Yasheng Wang, Xin Jiang, and Maosong Sun · 2021
Closest in time.
Watermarking graph neural networks by random graphs
Xiangyu Zhao, Hanzhou Wu, and Xinpeng Zhang · 2021
Closest in time.