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Graph convolutional networks (GCNs) have been very effective in addressing the issue of various graph-structured related tasks.
T. Xiao, Z. Chen, D. Wang, S. Wang, Learning how to propagate messages in graph neural networks, in: Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining, 2021, pp. 1894–1903
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P. D. Dobson, A. J. Doig, Distinguishing enzyme structures from non-enzymes without alignments, Journal of molecular biology 330 (4) (2003) 771–783
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I. Schomburg, A. Chang, C. Ebeling, M. Gremse, C. Heldt, G. Huhn, D. Schomburg, Brenda, the enzyme database: updates and major new developments, Nucleic acids research 32 (suppl_1) (2004) D431–D433
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K. M. Borgwardt, C. S. Ong, S. Schönauer, S. Vishwanathan, A. J. Smola, H.-P. Kriegel, Protein function prediction via graph kernels, Bioinformatics 21 (suppl_1) (2005) i47–i56
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K. Riesen, H. Bunke, Iam graph database repository for graph based pattern recognition and machine learning, in: Structural, Syntactic, and Statistical Pattern Recognition: Joint IAPR International Workshop, SSPR & SPR 2008, Orlando, USA, December 4-6, 2008. Proceedings, Springer, 2008, pp. 287–297
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N. Wale, I. A. Watson, G. Karypis, Comparison of descriptor spaces for chemical compound retrieval and classification, Knowledge and Information Systems 14 (2008) 347–375
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D. K. Duvenaud, D. Maclaurin, J. Iparraguirre, R. Bombarell, T. Hirzel, A. Aspuru-Guzik, R. P. Adams, Convolutional networks on graphs for learning molecular fingerprints, Advances in neural information processing systems 28 (2015)
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W. Hamilton, Z. Ying, J. Leskovec, Inductive representation learning on large graphs, Advances in neural information processing systems 30 (2017)
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H. Dai, H. Li, T. Tian, X. Huang, L. Wang, J. Zhu, L. Song, Adversarial attack on graph structured data, in: International conference on machine learning, PMLR, 2018, pp. 1115–1124
2018
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D. Zügner, A. Akbarnejad, S. Günnemann, Adversarial attacks on neural networks for graph data, in: Proceedings of the 24th ACM SIGKDD international conference on knowledge discovery & data mining, 2018, pp. 2847–2856
2018
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T. Gu, K. Liu, B. Dolan-Gavitt, S. Garg, Badnets: Evaluating backdooring attacks on deep neural networks, IEEE Access 7 (2019) 47230–47244
2019
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E. Bagdasaryan, A. Veit, Y. Hua, D. Estrin, V. Shmatikov, How to backdoor federated learning, in: International conference on artificial intelligence and statistics, PMLR, 2020, pp. 2938–2948
2020
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S. Tao, Q. Cao, H. Shen, J. Huang, Y. Wu, X. Cheng, Single node injection attack against graph neural networks, in: Proceedings of the 30th ACM International Conference on Information & Knowledge Management, 2021, pp. 1794–1803
2021
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Z. Xi, R. Pang, S. Ji, T. Wang, Graph backdoor, in: 30th USENIX Security Symposium (USENIX Security 21), 2021, pp. 1523–1540
2021
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2021
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Y. Li, Y. Jiang, Z. Li, S.-T. Xia, Backdoor learning: A survey, IEEE Transactions on Neural Networks and Learning Systems (2022)
2022
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Y. Liu, X. Ma, J. Bailey, F. Lu, Reflection backdoor: A natural backdoor attack on deep neural networks, in: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part X 16, Springer, 2020, pp. 182–199
2020
Cited alongside, same era.
2020
Cited alongside, same era.
2020
Cited alongside, same era.
2020
Cited alongside, same era.
E. Bagdasaryan, V. Shmatikov, Blind backdoors in deep learning models, in: 30th USENIX Security Symposium (USENIX Security 21), 2021, pp. 1505–1521
2021
Cited alongside, same era.
2022
Later among the works it cites.
J. Dai, W. Zhu, X. Luo, A targeted universal attack on graph convolutional network by using fake nodes, Neural Processing Letters 54 (4) (2022) 3321–3337
2022
Later among the works it cites.
S. Yang, B. G. Doan, P. Montague, O. De Vel, T. Abraham, S. Camtepe, D. C. Ranasinghe, S. S. Kanhere, Transferable graph backdoor attack, in: Proceedings of the 25th International Symposium on Research in Attacks, Intrusions and Defenses, 2022, pp. 321–332
2022
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2022
Later among the works it cites.
H. Zheng, H. Xiong, J. Chen, H. Ma, G. Huang, Motif-backdoor: Rethinking the backdoor attack on graph neural networks via motifs, IEEE Transactions on Computational Social Systems (2023)
2023
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