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Long-range interactions are essential for the correct description of complex systems in many scientific fields.
The folded normal distribution
Leone, F. C., Nelson, L. S., and Nottingham, R. B · 1961
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Information about hyperparamters in hierarchical models
Goel, P. K. and Degroot, M. H · 1981
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Learning representations by back-propagating errors
Rumelhart, D. E., Hinton, G. E., and Williams, R. J · 1986
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The cascade-correlation learning architecture
Fahlman, S. and Lebiere, C · 1989
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Supervised neural networks for the classification of structures
Sperduti, A. and Starita, A · 1997
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An introduction to variational methods for graphical models
Jordan, M. I., Ghahramani, Z., Jaakkola, T. S., and Saul, L. K · 1999
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Block of hac1 mrna translation by long-range base pairing is released by cytoplasmic splicing upon induction of the unfolded protein response
Rüegsegger, U., Leber, J. H., and Walter, P · 2001
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The discrete normal distribution
Roy, D · 2003
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Overdispersed and underdispersed poisson generalizations
del Castillo, J. and Pérez-Casany, M · 2005
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Universal approximation capability of cascade correlation for structures
Hammer, B., Micheli, A., and Sperduti, A · 2005
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Collapsed variational dirichlet process mixture models
Kurihara, K., Welling, M., and Teh, Y. W · 2007
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Accurate and efficient corrections for missing dispersion interactions in molecular simulations
Shirts, M. R., Mobley, D. L., Chodera, J. D., and Pande, V. S · 2007
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Bayesian theory , volume 405
Bernardo, J. M. and Smith, A. F · 2009
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Neural network for graphs: A contextual constructive approach
Micheli, A · 2009
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The graph neural network model
Scarselli, F., Gori, M., Tsoi, A. C., Hagenbuchner, M., and Monfardini, G · 2009
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T-cell receptors binding orientation over peptide/mhc class i is driven by long-range interactions
Ferber, M., Zoete, V., and Michielin, O · 2012
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Modeling underdispersed count data with generalized poisson regression
Harris, T., Yang, Z., and Hardin, J. W · 2012
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Evaluating the effects of cutoffs and treatment of long-range electrostatics in protein folding simulations
Piana, S., Lindorff-Larsen, K., Dirks, R. M., Salmon, J. K., Dror, R. O., and Shaw, D. E · 2012
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Physics of long-range interacting systems
Campa, A., Dauxois, T., Fanelli, D., and Ruffo, S · 2014
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Variational inference: A review for statisticians
Blei, D. M., Kucukelbir, A., and McAuliffe, J. D · 2017
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Bresson, X. and Laurent, T · 2017
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Inductive representation learning on large graphs
Hamilton, W., Ying, Z., 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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Contextual Graph Markov Model: A deep and generative approach to graph processing
Bacciu, D., Errica, F., and Micheli, A · 2018
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Recursive neural networks for density estimation over generalized random graphs
Bongini, M., Rigutini, L., and Trentin, E · 2018
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Deeper insights into graph convolutional networks for semi-supervised learning
Li, Q., Han, Z., and Wu, X.-M · 2018
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Graph attention networks
Velickovic, P., Cucurull, G., Casanova, A., Romero, A., Lio, 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
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Learning discrete structures for graph neural networks
Franceschi, L., Niepert, M., Pontil, M., and He, X · 2019
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Diffusion improves graph learning
Gasteiger, J., Weißenberger, S., and Günnemann, S · 2019
Cited alongside, same era.
DeepGCNs: Can GCNs go as deep as CNNs?
Li, G., Muller, M., Thabet, A., and Ghanem, B · 2019
Cited alongside, same era.
How powerful are graph neural networks?
Xu, K., Hu, W., Leskovec, J., and Jegelka, S · 2019
Cited alongside, same era.
Gnn-film: Graph neural networks with feature-wise linear modulation
Brockschmidt, M · 2020
Cited alongside, same era.
Principal neighbourhood aggregation for graph nets
Corso, G., Cavalleri, L., Beaini, D., Liò, P., and Veličković, P · 2020
Cited alongside, same era.
Sign: Scalable inception graph neural networks
Frasca, F., Rossi, E., Eynard, D., Chamberlain, B., Bronstein, M., and Monti, F · 2020
Cited alongside, same era.
Graph-coupled oscillator networks
Rusch, T. K., Chamberlain, B., Rowbottom, J., Mishra, S., and Bronstein, M · 2022
Later among the works it cites.
Understanding over-squashing and bottlenecks on graphs via curvature
Topping, J., Giovanni, F. D., Chamberlain, B. P., Dong, X., and Bronstein, M. M · 2022
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Leave graphs alone: Addressing over-squashing without rewiring
Tortorella, D. and Micheli, A · 2022
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Capturing graphs with hypo-elliptic diffusions
Toth, C., Lee, D., Hacker, C., and Oberhauser, H · 2022
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Auto-gnn: Neural architecture search of graph neural networks
Zhou, K., Huang, X., Song, Q., Chen, R., and Hu, X · 2022
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Rewiring networks for graph neural network training using discrete geometry
Bober, J., Monod, A., Saucan, E., and Webster, K. N · 2023
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Bayesian graph neural networks with adaptive connection sampling
Hasanzadeh, A., Hajiramezanali, E., Boluki, S., Zhou, M., Duffield, N., Narayanan, K., and Qian, X · 2020
Cited alongside, same era.
Strategies for pre-training graph neural networks
Hu, W., Liu, B., Gomes, J., Zitnik, M., Liang, P., Pande, V., and Leskovec, J · 2020
Cited alongside, same era.
Graph neural networks exponentially lose expressive power for node classification
Oono, K. and Suzuki, T · 2020
Cited alongside, same era.
Dropedge: Towards deep graph convolutional networks on node classification
Rong, Y., Huang, W., Xu, T., and Huang, J · 2020
Cited alongside, same era.
Learning to simulate complex physics with graph networks
Sanchez-Gonzalez, A., Godwin, J., Pfaff, T., Ying, R., Leskovec, J., and Battaglia, P · 2020
Cited alongside, same era.
Adaptive propagation graph convolutional network
Spinelli, I., Scardapane, S., and Uncini, A · 2020
Cited alongside, same era.
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Investigating the interplay between features and structures in graph learning
Castellana, D. and Errica, F · 2023
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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. M · 2023
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On class distributions induced by nearest neighbor graphs for node classification of tabular data
Errica, F · 2023
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GwAC: GNNs with asynchronous communication
Faber, L. and Wattenhofer, R · 2023
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Ugsl: A unified framework for benchmarking graph structure learning
Fatemi, B., Abu-El-Haija, S., Tsitsulin, A., Kazemi, M., Zelle, D., Bulut, N., Halcrow, J., and Perozzi, B · 2023
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Rewiring with positional encodings for graph neural networks
Gabrielsson, R. B., Yurochkin, M., and Solomon, J · 2023
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Anti-symmetric DGN: a stable architecture for deep graph networks
Gravina, A., Bacciu, D., and Gallicchio, C · 2023
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Drew: Dynamically rewired message passing with delay
Gutteridge, B., Dong, X., Bronstein, M. M., and Di Giovanni, F · 2023
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A generalization of vit/mlp-mixer to graphs
He, X., Hooi, B., Laurent, T., Perold, A., LeCun, Y., and Bresson, X · 2023
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FoSR: First-order spectral rewiring for addressing oversquashing in GNNs
Karhadkar, K., Banerjee, P. K., and Montufar, G · 2023
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Graph inductive biases in transformers without message passing
Ma, L., Lin, C., Lim, D., Romero-Soriano, A., Dokania, P. K., Coates, M., Torr, P., and Lim, S.-N · 2023
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A survey on oversmoothing in graph neural networks
Rusch, T. K., Bronstein, M. M., and Mishra, S · 2023
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Exphormer: Sparse transformers for graphs
Shirzad, H., Velingker, A., Venkatachalam, B., Sutherland, D. J., and Sinop, A. K · 2023
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Opengsl: A comprehensive benchmark for graph structure learning
Zhou, Z., Zhou, S., Mao, B., Zhou, X., Chen, J., Tan, Q., Zha, D., Feng, Y., Chen, C., and Wang, C · 2023
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Locality-aware graph rewiring in GNNs
Barbero, F., Velingker, A., Saberi, A., Bronstein, M. M., and Giovanni, F. D · 2024
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Recurrent distance filtering for graph representation learning
Ding, Y., Orvieto, A., He, B., and Hofmann, T · 2024
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Cooperative graph neural networks
Finkelshtein, B., Huang, X., Bronstein, M. M., and Ceylan, I. I · 2024
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Processing large-scale graphs with g-signatures
Gruber, L., Schäfl, B., Brandstetter, J., and Hochreiter, S · 2024
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Long-short-range message-passing: A physics-informed framework to capture non-local interaction for scalable molecular dynamics simulation
Li, Y., Wang, Y., Huang, L., Yang, H., Wei, X., Zhang, J., Wang, T., Wang, Z., Shao, B., and Liu, T.-Y · 2024
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Attending to graph transformers
Müller, L., Galkin, M., Morris, C., and Rampášek, L · 2024
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Non-backtracking graph neural networks
Park, S., Ryu, N., Kim, G., Woo, D., Yun, S.-Y., and Ahn, S · 2024
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Graph-mamba: Towards long-range graph sequence modeling with selective state spaces
Wang, C., Tsepa, O., Ma, J., and Wang, B · 2024
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Port-hamiltonian architectural bias for long-range propagation in deep graph networks
Heilig, S., Gravina, A., Trenta, A., Gallicchio, C., and Bacciu, D · 2025
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