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Graph neural networks (GNNs) achieve remarkable performance for tasks on graph data.
Adamic, L.: The political blogosphere and the 2004 u.s. election: Divided they blog. Proceedings of the 3rd International Workshop on Link Discovery (04 2005). https://doi.org/10.1145/1134271.1134277
2005
Earlier work this paper cites.
Sen, P., Namata, G., Bilgic, M., Getoor, L., Gallagher, B., Eliassi-Rad, T.: Collective classification in network data. AI Magazine 29
2008
Earlier work this paper cites.
2014
Earlier work this paper cites.
Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. CoRR abs/1412.6980
2014
Earlier work this paper cites.
2016
Earlier work this paper cites.
Kipf, T., Welling, M.: Variational graph auto-encoders. ArXiv abs/1611.07308
2016
Earlier work this paper cites.
Fout, A., Byrd, J., Shariat, B., Ben-Hur, A.: Protein interface prediction using graph convolutional networks. In: NIPS (2017)
2017
Earlier work this paper cites.
2017
Earlier work this paper cites.
Meng, D., Chen, H.: Magnet: A two-pronged defense against adversarial examples. In: CCS ’17 (2017)
2017
Earlier work this paper cites.
2017
Earlier work this paper cites.
2018
Cited alongside, same era.
2018
Cited alongside, same era.
2018
Cited alongside, same era.
Rhee, S., Seo, S., Kim, S.: Hybrid approach of relation network and localized graph convolutional filtering for breast cancer subtype classification. In: IJCAI (2018)
2018
Cited alongside, same era.
Hwang, U., Park, J., Jang, H., Yoon, S., Cho, N.I.: Puvae: A variational autoencoder to purify adversarial examples. IEEE Access 7
2019
Later among the works it cites.
Wang, B., Gong, N.Z.: Attacking graph-based classification via manipulating the graph structure. In: CCS ’19 (2019)
2019
Later among the works it cites.
Wang, J., Wen, R., Wu, C., Huang, Y., Xion, J.: Fdgars: Fraudster detection via graph convolutional networks in online app review system. In: WWW ’19 (2019)
2019
Later among the works it cites.
Wu, H., Wang, C., Tyshetskiy, Y., Docherty, A., Lu, K., Zhu, L.: Adversarial examples for graph data: Deep insights into attack and defense. In: IJCAI (2019)
2019
Later among the works it cites.
Xu, K., Chen, H., Liu, S., Chen, P.Y., Weng, T.W., Hong, M., Lin, X.: Topology attack and defense for graph neural networks: An optimization perspective. In: IJCAI (2019)
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2018
Cited alongside, same era.
Zhang, D., Yin, J., Zhu, X., Zhang, C.: Sine: Scalable incomplete network embedding. 2018 IEEE International Conference on Data Mining (ICDM) pp. 737–746 (2018)
2018
Cited alongside, same era.
Zhuang, C., Ma, Q.: Dual graph convolutional networks for graph-based semi-supervised classification. In: WWW (2018)
2018
Cited alongside, same era.
Zügner, D., Akbarnejad, A., Günnemann, S.: Adversarial attacks on neural networks for graph data. Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (2018)
2018
Cited alongside, same era.
2019
Cited alongside, same era.
2019
Later among the works it cites.
Zhu, D., Zhang, Z., Cui, P., Zhu, W.: Robust graph convolutional networks against adversarial attacks. Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (2019)
2019
Later among the works it cites.
Zugner, D., Gunnemann, S.: Adversarial attacks on graph neural networks via meta learning (2019)
2019
Later among the works it cites.
Entezari, N., Al-Sayouri, S.A., Darvishzadeh, A., Papalexakis, E.E.: All you need is low (rank): Defending against adversarial attacks on graphs. Proceedings of the 13th International Conference on Web Search and Data Mining (2020)
2020
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