Fetching the paper…
Reading the bibliography…
Spectral Graph Neural Networks (GNNs) are gaining attention for their ability to surpass the limitations of message-passing GNNs.
Introduction to numerical analysis
Hildebrand, F. B · 1987
Earlier work this paper cites.
Deep graph contrastive representation learning
Zhu, Y., Xu, Y., Yu, F., Liu, Q., Wu, S., and Wang, L · 2006
Earlier work this paper cites.
Collective classification in network data
Sen, P., Namata, G., Bilgic, M., Getoor, L., Galligher, B., and Eliassi-Rad, T · 2008
Earlier work this paper cites.
Social structure of facebook networks
Traud, A. L., Mucha, P. J., and Porter, M. A · 2012
Earlier work this paper cites.
Convolutional neural networks on graphs with fast localized spectral filtering
Defferrard, M., Bresson, X., and Vandergheynst, P · 2016
Earlier work this paper cites.
Variational graph auto-encoders
Kipf, T. N. and Welling, M · 2016
Earlier work this paper cites.
Protein interface prediction using graph convolutional networks
Fout, A., Byrd, J., Shariat, B., and Ben-Hur, A · 2017
Earlier work this paper cites.
Neural message passing for quantum chemistry
Gilmer, J., Schoenholz, S. S., Riley, P. F., Vinyals, O., and Dahl, G. E · 2017
Earlier work this paper cites.
Inductive representation learning on large graphs
Hamilton, W., Ying, Z., and Leskovec, J · 2017
Earlier work this paper cites.
Semi-supervised classification with graph convolutional networks
Kipf, T. N. and Welling, M · 2017
Earlier work this paper cites.
Contextual stochastic block models
Deshpande, Y., Sen, S., Montanari, A., and Mossel, E · 2018
Earlier work this paper cites.
Predict then propagate: Graph neural networks meet personalized pagerank
Klicpera, J., Bojchevski, A., and Günnemann, S · 2018
Earlier work this paper cites.
Cayleynets: Graph convolutional neural networks with complex rational spectral filters
Levie, R., Monti, F., Bresson, X., and Bronstein, M. M · 2018
Earlier work this paper cites.
Link prediction based on graph neural networks
Zhang, M. and Chen, Y · 2018
Earlier work this paper cites.
An end-to-end deep learning architecture for graph classification
Zhang, M., Cui, Z., Neumann, M., and Chen, Y · 2018
Cited alongside, same era.
Mixhop: Higher-order graph convolutional architectures via sparsified neighborhood mixing
Abu-El-Haija, S., Perozzi, B., Kapoor, A., Alipourfard, N., Lerman, K., Harutyunyan, H., Ver Steeg, G., and Galstyan, A · 2019
Cited alongside, same era.
Deep Graph Infomax
Veličković, P., Fedus, W., Hamilton, W. L., Liò, P., Bengio, Y., and Hjelm, R. D · 2019
Cited alongside, same era.
Geom-gcn: Geometric graph convolutional networks
Pei, H., Wei, B., Chang, K. C.-C., Lei, Y., and Yang, B · 2020
Cited alongside, same era.
Graph convolutional networks with markov random field reasoning for social spammer detection
Wu, Y., Lian, D., Xu, Y., Wu, L., and Chen, E · 2020
Cited alongside, same era.
Time and space complexity of graph convolutional networks
Blakely, D., Lanchantin, J., and Qi, Y · 2021
Wang, T., Wang, R., Jin, D., He, D., and Huang, Y · 2021
Later among the works it cites.
Infogcl: Information-aware graph contrastive learning
Xu, D., Cheng, W., Luo, D., Chen, H., and Zhang, X · 2021
Later among the works it cites.
Interpreting and unifying graph neural networks with an optimization framework
Zhu, M., Wang, X., Shi, C., Ji, H., and Cui, P · 2021
Later among the works it cites.
Label-wise graph convolutional network for heterophilic graphs
Dai, E., Zhou, S., Guo, Z., and Wang, S · 2022
Later among the works it cites.
Convolutional neural networks on graphs with chebyshev approximation, revisited
He, M., Wei, Z., and Wen, J.-R · 2022
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Beyond low-frequency information in graph convolutional networks
Bo, D., Wang, X., Shi, C., and Shen, H · 2021
Cited alongside, same era.
Adaptive universal generalized pagerank graph neural network
Chien, E., Peng, J., Li, P., and Milenkovic, O · 2021
Cited alongside, same era.
Beyond low-pass filters: Adaptive feature propagation on graphs
Li, S., Kim, D., and Wang, Q · 2021
Cited alongside, same era.
Expertise and dynamics within crowdsourced musical knowledge curation: A case study of the genius platform
Lim, D. and Benson, A. R · 2021
Cited alongside, same era.
Large scale learning on non-homophilous graphs: New benchmarks and strong simple methods
Lim, D., Hohne, F., Li, X., Huang, S. L., Gupta, V., Bhalerao, O., and Lim, S. N · 2021
Cited alongside, same era.
Rozemberczki, B. and Sarkar, R · 2021
Cited alongside, same era.
Li, X., Zhu, R., Cheng, Y., Shan, C., Luo, S., Li, D., and Qian, W · 2022
Later among the works it cites.
Revisiting graph contrastive learning from the perspective of graph spectrum
Liu, N., Wang, X., Bo, D., Shi, C., and Pei, J · 2022
Later among the works it cites.
Is homophily a necessity for graph neural networks?
Ma, Y., Liu, X., Shah, N., and Tang, J · 2022
Later among the works it cites.
How powerful are spectral graph neural networks
Wang, X. and Zhang, M · 2022
Later among the works it cites.
Graph neural networks in recommender systems: a survey
Wu, S., Sun, F., Zhang, W., Xie, X., and Cui, B · 2022
Later among the works it cites.
HP-GMN: graph memory networks for heterophilous graphs
Xu, J., Dai, E., Zhang, X., and Wang, S · 2022
Later among the works it cites.
Graph neural networks for graphs with heterophily: A survey
Zheng, X., Liu, Y., Pan, S., Zhang, M., Jin, D., and Yu, P. S · 2022
Later among the works it cites.
A survey on spectral graph neural networks
Bo, D., Wang, X., Liu, Y., Fang, Y., Li, Y., and Shi, C · 2023
Closest in time.