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Message passing graph neural networks (GNNs) are a popular learning architectures for graph-structured data.
Random walks and electric networks , volume 22
Doyle, P. G. and Snell, J. L · 1984
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Random walks on graphs
Lovász, L · 1993
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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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Laplacians of graphs and cheeger’s inequalities
Chung, F. R · 1996
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Algebraic potential theory on graphs
Biggs, N · 1997
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Spectral graph theory , volume 92
Chung, F. R · 1997
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Automating the construction of internet portals with machine learning
McCallum, A. K., Nigam, K., Rennie, J., and Seymore, K · 2000
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Nearly-linear time algorithms for graph partitioning, graph sparsification, and solving linear systems
Spielman, D. A. and Teng, S.-H · 2004
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Minimizing effective resistance of a graph
Ghosh, A., Boyd, S., and Saberi, A · 2008
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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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Biharmonic distance
Lipman, Y., Rustamov, R. M., and Funkhouser, T. A · 2010
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Graph sparsification by effective resistances
Spielman, D. A. and Srivastava, N · 2011
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Multiway spectral partitioning and higher-order cheeger inequalities
Lee, J. R., Gharan, S. O., and Trevisan, L · 2014
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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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Semi-supervised classification with graph convolutional networks
Kipf, T. N. and Welling, M · 2017
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Probability on trees and networks , volume 42
Lyons, R. and Peres, Y · 2017
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Graph clustering using effective resistance
Alev, V. L., Anari, N., Lau, L. C., and Oveis Gharan, S · 2018
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Relational inductive biases, deep learning, and graph networks
Battaglia, P. W., Hamrick, J. B., Bapst, V., Sanchez-Gonzalez, A., Zambaldi, V., Malinowski, M., Tacchetti, A., Raposo, D., Santoro, A., Faulkner, R., et al · 2018
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Representation learning on graphs with jumping knowledge networks
On the bottleneck of graph neural networks and its practical implications
Alon, U. and Yahav, E · 2021
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Ultrasparse ultrasparsifiers and faster laplacian system solvers
Jambulapati, A. and Sidford, A · 2021
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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 · 2021
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Diffwire: Inductive graph rewiring via the lovász bound
Arnaiz-Rodríguez, A., Begga, A., Escolano, F., and Oliver, N. M · 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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Expander graph propagation
Deac, A., Lackenby, M., and Veličković, P · 2022
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Xu, K., Li, C., Tian, Y., Sonobe, T., Kawarabayashi, K.-i., and Jegelka, S · 2018
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Diffusion improves graph learning
Gasteiger, J., Weißenberger, S., and Günnemann, S · 2019
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Spectral and algebraic graph theory
Spielman, D · 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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A note on over-smoothing for graph neural networks
Cai, C. and Wang, Y · 2020
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Tudataset: A collection of benchmark datasets for learning with graphs
Morris, C., Kriege, N. M., Bause, F., Kersting, K., Mutzel, P., and Neumann, M · 2020
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Graph neural networks exponentially lose expressive power for node classification
Oono, K. and Suzuki, T · 2020
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Long range graph benchmark
Dwivedi, V. P., Rampášek, L., Galkin, M., Parviz, A., Wolf, G., Luu, A. T., and Beaini, D · 2022
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Fosr: First-order spectral rewiring for addressing oversquashing in gnns, 2022
Karhadkar, K., Banerjee, P. K., and Montúfar, G · 2022
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Persistent laplacians: Properties, algorithms and implications
Mémoli, F., Wan, Z., and Wang, Y · 2022
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Affinity-aware graph networks, 2022
Velingker, A., Sinop, A. K., Ktena, I., Veličković, P., and Gollapudi, S · 2022
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On over-squashing in message passing neural networks: The impact of width, depth, and topology
Di Giovanni, F., Giusti, L., Barbero, F., Luise, G., Lio’, P., and Bronstein, M · 2023
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Minimizing the effective graph resistance by adding links is np-hard, 2023
Kooij, R. E. and Achterberg, M. A · 2023
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