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Message Passing Neural Networks (MPNNs) are instances of Graph Neural Networks that leverage the graph to send messages over the edges.
Resistances and currents in infinite electrical networks
Thomassen, C · 1990
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Random walks on graphs
Lovász, L · 1993
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Encoding labeled graphs by labeling raam
Sperduti, A · 1993
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Learning long-term dependencies with gradient descent is difficult
Bengio, Y., Simard, P., and Frasconi, P · 1994
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The electrical resistance of a graph captures its commute and cover times
Chandra, A. K., Raghavan, P., Ruzzo, W. L., Smolensky, R., and Tiwari, P · 1996
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Learning task-dependent distributed representations by backpropagation through structure
Goller, C. and Kuchler, A · 1996
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Spectral graph theory
Chung, F. R. and Graham, F. C · 1997
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Long short-term memory
Hochreiter, S. and Schmidhuber, J · 1997
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A new model for learning in graph domains
Gori, M., Monfardini, G., and Scarselli, F · 2005
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Ricci curvature of metric spaces
Ollivier, Y · 2007
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The graph neural network model
Scarselli, F., Gori, M., Tsoi, A. C., Hagenbuchner, M., and Monfardini, G · 2008
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Ricci curvature of markov chains on metric spaces
Ollivier, Y · 2009
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Effective graph resistance
Ellens, W., Spieksma, F. M., Van Mieghem, P., Jamakovic, A., and Kooij, R. E · 2011
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On the difficulty of training recurrent neural networks
Pascanu, R., Mikolov, T., and Bengio, Y · 2013
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Spectral networks and locally connected networks on graphs
Bruna, J., Zaremba, W., Szlam, A., and LeCun, Y · 2014
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Convolutional neural networks on graphs with fast localized spectral filtering
Defferrard, M., Bresson, X., and Vandergheynst, P · 2016
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Deep learning without poor local minima
Kawaguchi, K · 2016
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Spectrally-normalized margin bounds for neural networks
Bartlett, P. L., Foster, D. J., and Telgarsky, M. J · 2017
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Bresson, X. and Laurent, T · 2017
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Neural message passing for quantum chemistry
Gilmer, J., Schoenholz, S. S., Riley, P. F., Vinyals, O., and Dahl, G. E · 2017
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Inductive representation learning on large graphs
Hamilton, W. L., Ying, R., and Leskovec, J · 2017
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Semi-Supervised Classification with Graph Convolutional Networks
Kipf, T. N. and Welling, M · 2017
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Electrical networks and algebraic graph theory: Models, properties, and applications
Dörfler, F., Simpson-Porco, J. W., and Bullo, F · 2018
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Graph attention networks
Veličković, P., Cucurull, G., Casanova, A., Romero, A., Liò, P., and Bengio, Y · 2018
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Representation learning on graphs with jumping knowledge networks
Xu, K., Li, C., Tian, Y., Sonobe, T., Kawarabayashi, K.-i., and Jegelka, S · 2018
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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
Rethinking graph transformers with spectral attention
Kreuzer, D., Beaini, D., Hamilton, W., Létourneau, V., and Tossou, P · 2021
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Training graph neural networks with 1000 layers
Li, G., Müller, M., Ghanem, B., and Koltun, V · 2021
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Graphit: Encoding graph structure in transformers
Mialon, G., Chen, D., Selosse, M., and Mairal, J · 2021
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Do transformers really perform badly for graph representation?
Ying, C., Cai, T., Luo, S., Zheng, S., Ke, G., He, D., Shen, Y., and Liu, T.-Y · 2021
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Shortest path networks for graph property prediction
Abboud, R., Dimitrov, R., and Ceylan, I. I · 2022
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DiffWire: Inductive Graph Rewiring via the Lovász Bound
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The logical expressiveness of graph neural networks
Barceló, P., Kostylev, E. V., Monet, M., Pérez, J., Reutter, J., and Silva, J. P · 2019
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Diffusion improves graph learning
Klicpera, J., Weissenberger, S., and Günnemann, S · 2019
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Deepgcns: Can gcns go as deep as cnns?
Li, G., Muller, M., Thabet, A., and Ghanem, B · 2019
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Weisfeiler and leman go neural: Higher-order graph neural networks
Morris, C., Ritzert, M., Fey, M., Hamilton, W. L., Lenssen, J. E., Rattan, G., and Grohe, M · 2019
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Revisiting graph neural networks: All we have is low-pass filters
Nt, H. and Maehara, T · 2019
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How powerful are graph neural networks?
Xu, K., Hu, W., Leskovec, J., and Jegelka, S · 2019
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Arnaiz-Rodríguez, A., Begga, A., Escolano, F., and Oliver, N · 2022
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Oversquashing in gnns through the lens of information contraction and graph expansion
Banerjee, P. K., Karhadkar, K., Wang, Y. G., Alon, U., and Montúfar, G · 2022
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Neural sheaf diffusion: A topological perspective on heterophily and oversmoothing in GNNs
Bodnar, C., Giovanni, F. D., Chamberlain, B. P., Lió, P., and Bronstein, M. M · 2022
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Rewiring with positional encodings for graph neural networks
Brüel-Gabrielsson, R., Yurochkin, M., and Solomon, J · 2022
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Expander graph propagation
Deac, A., Lackenby, M., and Veličković, P · 2022
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Discrete curvature on graphs from the effective resistance
Devriendt, K. and Lambiotte, R · 2022
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Theory of graph neural networks: Representation and learning
Jegelka, S · 2022
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Fosr: First-order spectral rewiring for addressing oversquashing in gnns
Karhadkar, K., Banerjee, P. K., and Montúfar, G · 2022
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Recipe for a general, powerful, scalable graph transformer
Rampasek, L., Galkin, M., Dwivedi, V. P., Luu, A. T., Wolf, G., and Beaini, D · 2022
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Graph-coupled oscillator networks
Rusch, T. K., Chamberlain, B. P., Rowbottom, J., Mishra, S., and Bronstein, M. M · 2022
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Understanding over-squashing and bottlenecks on graphs via curvature
Topping, J., Di Giovanni, F., Chamberlain, B. P., Dong, X., and Bronstein, M. M · 2022
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Velingker, A., Sinop, A. K., Ktena, I., Veličković, P., and Gollapudi, S · 2022
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Analysis of graph neural networks with theory of markov chains
Zhao, W., Wang, C., Han, C., and Guo, T · 2022
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Understanding oversquashing in gnns through the lens of effective resistance
Black, M., Nayyeri, A., Wan, Z., and Wang, Y · 2023
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Drew: Dynamically rewired message passing with delay
Gutteridge, B., Dong, X., Bronstein, M., and Di Giovanni, F · 2023
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