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This paper introduces a novel Framelet Graph approach based on p-Laplacian GNN.
Sweldens W (1998) The lifting scheme: A construction of second generation wavelets. SIAM Journal on Mathematical Analysis 29(2):511–546, DOI 10.1137/S0036141095289051
1998
Earlier work this paper cites.
Tenenbaum JB, Silva Vd, Langford JC (2000) A global geometric framework for nonlinear dimensionality reduction. science 290(5500):2319–2323
2000
Earlier work this paper cites.
Burda Z, Correia J, Krzywicki A (2001) Statistical ensemble of scale-free random graphs. Physical Review E 64(4):046118
2001
Earlier work this paper cites.
Zhang Q, Gu Y, Mateusz M, Baktashmotlagh M, Eriksson A (2003) Implicitly defined layers in neural networks. arxiv200301822
2003
Earlier work this paper cites.
Belkin M, Matveeva I, Niyogi P (2004) Tikhonov regularization and semi-supervised learning on large graphs. In: 2004 IEEE International Conference on Acoustics, Speech, and Signal Processing, IEEE, vol 3, pp iii–1000
2004
Earlier work this paper cites.
Ly I (2005) The first eigenvalue for the p-laplacian operator. JIPAM J Inequal Pure Appl Math 6:91
2005
Earlier work this paper cites.
Zhou D, Schölkopf B (2005) Regularization on discrete spaces. In: The 27th DAGM Symposium,, August 31 - September 2, 2005, Vienna, Austria, vol 3663, pp 361–368
2005
Earlier work this paper cites.
Leskovec J, Faloutsos C (2006) Sampling from large graphs. In: Proceedings of the 12th ACM International Conference on Knowledge Discovery and Data Mining, p 631–636, DOI 10.1145/1150402.1150479
2006
Earlier work this paper cites.
Pandit S, Chau DH, Wang S, Faloutsos C (2007) Netprobe: a fast and scalable system for fraud detection in online auction networks. In: Proceedings of the 16th international conference on World Wide Web, pp 201–210
2007
Earlier work this paper cites.
Scarselli F, Gori M, Tsoi AC, Hagenbuchner M, Monfardini G (2009) The graph neural network model. IEEE Transactions on Neural Networks 20(1):61–80
2009
Earlier work this paper cites.
Liu G, Lin Z, Yu Y (2010) Robust subspace segmentation by low-rank representation. In: Proceedings of the 27th international conference on machine learning (ICML-10), pp 663–670
2010
Earlier work this paper cites.
Luo D, Huang H, Ding CHQ, Nie F (2010) On the eigenvectors of p p -Laplacian. Machine Learning 81(1):37–51
2010
Earlier work this paper cites.
Candès EJ, Li X, Ma Y, Wright J (2011) Robust principal component analysis? Journal of the ACM (JACM) 58(3):1–37
2011
Earlier work this paper cites.
Hammond DK, Vandergheynst P, Gribonval R (2011) Wavelets on graphs via spectral graph theory. Applied and Computational Harmonic Analysis (2):129–150
2011
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 International Conference on Learning Representations
2014
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, DOI 10.1016/j.acha.2015.09.005
2015
Earlier work this paper cites.
He X, Kempe D (2015) Stability of influence maximization. In: Proceedings of the 20th ACM International Conference on Knowledge Discovery and Data Mining, p 1256–1265
2015
Earlier work this paper cites.
Ciotti V, Bonaventura M, Nicosia V, Panzarasa P, Latora V (2016) Homophily and missing links in citation networks. EPJ Data Science 5:1–14
2016
Earlier work this paper cites.
Defferrard M, Bresson X, Vandergheynst P (2016) Convolutional neural networks on graphs with fast localized spectral filtering. Advances in Neural Information Processing Systems 29
2016
Earlier work this paper cites.
Bronstein MM, Bruna J, LeCun Y, Szlam A, Vandergheynst P (2017) Geometric deep learning: going beyond Euclidean data. IEEE Signal Processing Magazine 34(4):18–42
2017
Cited alongside, same era.
Gilmer J, Schoenholz SS, Riley PF, Vinyals O, Dahl GE (2017) Neural message passing for quantum chemistry. In: International Conference on Machine Learning, PMLR, pp 1263–1272
2017
Cited alongside, same era.
Kipf TN, Welling M (2017) Semi-supervised classification with graph convolutional networks. In: Proceedings of International Conference on Learning Representations
2017
Cited alongside, same era.
Hu X, Sun Y, Gao J, Hu Y, Yin B (2018) Locality preserving projection based on F-norm. In: Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence, pp 1330–1337
2018
Cited alongside, same era.
Chien E, Peng J, Li P, Milenkovic O (2021) Adaptive universal generalized pagerank graph neural network. In: Proceedings of International Conference on Learning Representations
2021
Later among the works it cites.
He M, Wei Z, Xu H, et al (2021) Bernnet: Learning arbitrary graph spectral filters via bernstein approximation. Advances in Neural Information Processing Systems 34:14239–14251
2021
Later among the works it cites.
Ma Y, Liu X, Zhao T, Liu Y, Tang J, Shah N (2021) A unified view on graph neural networks as graph signal denoising. URL https://openreview.net/forum?id=MD3D5UbTcb1
2021
Later among the works it cites.
Park J, Choo J, Park J (2021) Convergent graph solvers. In: Proceedings of International Conference on Learning Representations
2021
Later among the works it cites.
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2018
Cited alongside, same era.
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
Cited alongside, same era.
Bozorgnia F, Mohammadi SA, Vejchodskỳ T (2019) The first eigenvalue and eigenfunction of a nonlinear elliptic system. Applied Numerical Mathematics 145:159–174
2019
Cited alongside, same era.
Cheng T, Wang B (2020) Graph and total variation regularized low-rank representation for hyperspectral anomaly detection. IEEE Transactions on Geoscience and Remote Sensing 58(1):391–406, DOI 10.1109/TGRS.2019.2936609
2019
Cited alongside, same era.
Fan W, Ma Y, Li Q, He Y, Zhao E, Tang J, Yin D (2019) Graph neural networks for social recommendation. In: Proceedings of WWW, pp 417–426
2019
Cited alongside, same era.
Gasteiger J, Bojchevski A, Günnemann S (2019) Predict then propagate: Graph neural networks meet personalized pagerank. In: Proceedings of International Conference on Learning Representations
2019
Cited alongside, same era.
Wu F, Zhang T, Souza AHd, Fifty C, Yu T, Weinberger KQ (2019) Simplifying graph convolutional networks. In: Proceedings of International Conference on Machine Learning
2019
Cited alongside, same era.
Gu F, Chang H, Zhu W, Sojoudi S, El Ghaoui L (2020) Implicit graph neural networks. In: Advances in Neural Information Processing Systems
2020
Cited alongside, same era.
2021
Later among the works it cites.
Zhu M, Wang X, Shi C, Ji H, Cui P (2021) Interpreting and unifying graph neural networks with an optimization framework. In: Proceedings of WWW
2021
Later among the works it cites.
Frecon J, Gasso G, Pontil M, Salzo S (2022) Bregman neural networks. In: International Conference on Machine Learning, PMLR, pp 6779–6792
2022
Closest in time.
Fu G, Zhao P, Bian Y (2022) p p -Laplacian based graph neural networks. In: Proceedings of the 39th International Conference on Machine Learning, PMLR, vol 162, pp 6878–6917
2022
Closest in time.
Han A, Shi D, Shao Z, Gao J (2022) Generalized energy and gradient flow via graph framelets. arXiv:221004124
2022
Closest in time.
Li J, Lin S, Blanchet J, Nguyen VA (2022) Tikhonov regularization is optimal transport robust under martingale constraints. 2210.01413
2022
Closest in time.
Liu A, Li B, Li T, Zhou P, Wang R (2022) An-gcn: An anonymous graph convolutional network against edge-perturbing attacks. IEEE Transactions on Neural Networks and Learning Systems pp 1–15, DOI 10.1109/TNNLS.2022.3172296
2022
Closest in time.
Wu S, Sun F, Zhang W, Xie X, Cui B (2022) Graph neural networks in recommender systems: a survey. ACM Computing Surveys to appear
2022
Closest in time.
Yang M, Zheng X, Yin J, Gao J (2022) Quasi-Framelets: Another improvement to graph neural networks. arXiv:220104728 2201.04728
2022
Closest in time.
Zheng X, Zhou B, Wang YG, Zhuang X (2022) Decimated framelet system on graphs and fast g-framelet transforms. Journal of Machine Learning Research 23:18–1
2022
Closest in time.
Zou C, Han A, Lin L, Gao J (2022) A simple yet effective SVD-GCN for directed graphs. arxiv220509335
2022
Closest in time.
Lin L, Gao J (2023) A magnetic framelet-based convolutional neural network for directed graphs. In: ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp 1–5, DOI 10.1109/ICASSP49357.2023.10097148
2023
Closest in time.
Oka T, Yamada T (2023) Topology optimization method with nonlinear diffusion. Computer Methods in Applied Mechanics and Engineering 408:115940, DOI https://doi.org/10.1016/j.cma.2023.115940
2023
Closest in time.
Wen H, Lin Y, Xia Y, Wan H, Zimmermann R, Liang Y (2023) Diffstg: Probabilistic spatio-temporal graph forecasting with denoising diffusion models. arXiv preprint arXiv:230113629
2023
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