Quasi-Framelets: Robust Graph Neural Networks via Adaptive Framelet Convolution

Chen J, Wu Y, Lin X, Xuan Q (2019) Can adversarial network attack be defended? arXiv: 1903.05994

1903

Earlier work this paper cites.

Fu G, Hou Y, Zhang J, Ma K, Kamhoua BF, Cheng J (2020) Understanding graph neural networks from graph signal denoising perspectives. arXiv: 2006.04386

2006

Earlier work this paper cites.

Bruna J, Zaremba W, Szlam A, LeCun Y (2014) Spectral networks and locally connected networks on graphs. In: Proceedings of the International Conferenceon Learning Representations

2014

Earlier work this paper cites.

Duvenaud DK, Maclaurin D, Iparraguirre J, Bombarell R, Hirzel T, Aspuru-Guzik A, Adams RP (2015) Convolutional networks on graphs for learning molecular fingerprints. In: Advances in Neural Information Processing Systems, vol 28

2015

Earlier work this paper cites.

Defferrard M, Bresson X, Vandergheynst P (2016) Convolutional neural networks on graphs with fast localized spectral filtering. In: Advances in Neural Information Processing Systems, vol 29, pp 3844–3852

2016

Earlier work this paper cites.

Bojchevski A, Matkovic Y, Günnemann S (2017) Robust spectral clustering for noisy data: Modeling sparse corruptions improves latent embeddings. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp 737–746

2017

Earlier work this paper cites.

Dong B (2017) Sparse representation on graphs by tight wavelet frames and applications. Applied and Computational Harmonic Analysis 42(3):452–479

2017

Earlier work this paper cites.

Hamilton W, Ying Z, Leskovec J (2017) Inductive representation learning on large graphs. In: Advances in Neural Information Processing Systems, pp 1024–1034

2017

Earlier work this paper cites.

Kipf TN, Welling M (2017) Semi-supervised classification with graph convolutional networks. In: Proceedings of International Conference on Learning Representation, pp 1–14

2017

Earlier work this paper cites.

Veličković P, Cucurull G, Casanova A, Romero A, Lio P, Bengio Y (2018) Graph attention networks. In: Proceedings of International Conference on Learning Representations

2018

Earlier work this paper cites.

Yu B, Yin H, Zhu Z (2018) Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting. In: Proceedings of the 27th International Joint Conference on Artificial Intelligence (IJCAI)

2018

Earlier work this paper cites.

Zügner D, Akbarnejad A, Günnemann S (2018) Adversarial attacks on neural networks for graph data. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp 2847–2856

Original

2018

Earlier work this paper cites.

Velićković P, Fedus W, L W, Hamilton, Lió P, Bengio Y, DevonHjelm R (2019) Deep graph infomax. In: Proceedings of International Conference on Learning Representations

2019

Cited alongside, same era.

Zhu D, Zhang Z, Cui P, Zhu W (2019) Robust graph convolutional networks against adversarial attacks. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, Association for Computing Machinery, New York, NY, USA, KDD ’19, p 1399–1407, DOI 10.1145/3292500.3330851

2019

Cited alongside, same era.

Zügner D, Günnemann S (2019) Certifiable robustness and robust training for graph convolutional networks. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp 246–256

2019

Cited alongside, same era.

Jin W, Ma Y, Liu X, Tang X, Wang S, Tang J (2020) Graph structure learning for robust graph neural networks. In: Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp 66–74

Zheng X, Zhou B, Gao J, Wang Y, Lió P, Li M, Montúfar G (2021) How framelets enhance graph neural networks. In: Proceedings of the 38th International Conference on Machine Learning, vol 139, pp 12761–12771

2021

Later among the works it cites.

Di Giovanni F, Rowbottom J, Chamberlain BP, Markovich T, Bronstein MM (2022) Graph neural networks as gradient flows: Understanding graph convolutions via energy. arXiv preprint arXiv:220610991

2022

Closest in time.

Han A, Shi D, Shao Z, Gao J (2022) Generalized energy and gradient flow via graph framelets. arXiv preprint arXiv:221004124

2022

Closest in time.

Sun L, Dou Y, Yang C, Zhang K, Wang PS Jiand Yu, He L, Li B (2023a) Adversarial attack and defense on graph data: A survey. IEEE Transactions on Knowledge and Data Engineering 35(8):7693–7711, DOI 10.1109/TKDE.2022.3201243

2022

Closest in time.