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Graph Neural Networks (GNNs) have demonstrated remarkable success in learning from graph-structured data.
The nature of. pi.-. pi. interactions
Christopher A Hunter and Jeremy KM Sanders · 1990
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Structure-activity relationship of mutagenic aromatic and heteroaromatic nitro compounds. correlation with molecular orbital energies and hydrophobicity
Asim Kumar Debnath, Rosa L Lopez de Compadre, Gargi Debnath, Alan J Shusterman, and Corwin Hansch · 1991
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Autonomic healing of polymer composites
Scott R White, Nancy R Sottos, Philippe H Geubelle, Jeffrey S Moore, Michael R Kessler, SR Sriram, Eric N Brown, and S Viswanathan · 2001
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Mimicking photosynthetic solar energy transduction
Devens Gust, Thomas A Moore, and Ana L Moore · 2001
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Self-assembly at all scales
George M Whitesides and Bartosz Grzybowski · 2002
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Interactions with aromatic rings in chemical and biological recognition
Emmanuel A. Meyer, Ronald K. Castellano, and François Diederich · 2003
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On graph kernels: Hardness results and efficient alternatives
Thomas Gärtner, Peter A. Flach, and Stefan Wrobel · 2003
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Statistical evaluation of the predictive toxicology challenge 2000–2001
Toivonen Hannu, Ashwin Srinivasan, Ross D. King, Stefan Kramer, and Christoph Helma · 2003
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A new model for learning in graph domains
Marco Gori, Gabriele Monfardini, and Franco Scarselli · 2005
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Metal- organic frameworks with exceptionally high capacity for storage of carbon dioxide at room temperature
Andrew R Millward and Omar M Yaghi · 2005
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Long-range electron transfer
Harry B Gray and Jay R Winkler · 2005
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Protein function prediction via graph kernels
Karsten M. Borgwardt, Cheng Soon Ong, Stefan Schönauer, S. V. N. Vishwanathan, Alex J. Smola, and Hans-Peter Kriegel · 2005
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Evidence for wavelike energy transfer through quantum coherence in photosynthetic systems
Gregory S Engel, Tessa R Calhoun, Elizabeth L Read, Tae-Kyu Ahn, Tomáš Mančal, Yuan-Chung Cheng, Robert E Blankenship, and Graham R Fleming · 2007
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The graph neural network model
Franco Scarselli, Marco Gori, Antonio C. Tsoi, Markus Hagenbuchner, and Gabriele Monfardini · 2008
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Comparison of descriptor spaces for chemical compound retrieval and classification
Nikil Wale, Ian A. Watson, and George Karypis · 2008
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Luminescent metal–organic frameworks
Mark D Allendorf, Christina A Bauer, RK Bhakta, and RJT Houk · 2009
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Metal–organic framework materials as catalysts
JeongYong Lee, Omar K Farha, John Roberts, Karl A Scheidt, SonBinh T Nguyen, and Joseph T Hupp · 2009
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Photosynthetic energy conversion: natural and artificial
James Barber · 2009
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Efficient graphlet kernels for large graph comparison
Nino Shervashidze, SVN Vishwanathan, Tobias Petri, Kurt Mehlhorn, and Karsten Borgwardt · 2009
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Molecular switches
Ben L Feringa and Wesley R Browne · 2011
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Weisfeiler-lehman graph kernels
Nino Shervashidze, Pascal Schweitzer, Erik Jan van Leeuwen, Kurt Mehlhorn, and Karsten M. Borgwardt · 2011
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The infrared spectra of polycyclic aromatic hydrocarbons with excess peripheral h atoms (hn-pahs) and their relation to the 3.4 and 6.9 μ \mu m pah emission features
Scott A Sandford, Max P Bernstein, and Christopher K Materese · 2013
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Dropout: a simple way to prevent neural networks from overfitting
Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov · 2014
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Signal transduction in cancer
Richard Sever and Joan S Brugge · 2015
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Supramolecular systems chemistry
Elio Mattia and Sijbren Otto · 2015
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
Sergey Ioffe and Christian Szegedy · 2015
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2015
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ZINC 15 – ligand discovery for everyone
Teague Sterling and John J. Irwin · 2015
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Two’s company, three (or more) is a simplex
Chad Giusti, Robert Ghrist, and Danielle S. Bassett · 2016
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SATPdb: a database of structurally annotated therapeutic peptides
Sandeep Singh, Kumardeep Chaudhary, Sandeep Kumar Dhanda, Sherry Bhalla, Salman Sadullah Usmani, Ankur Gautam, Abhishek Tuknait, Piyush Agrawal, Deepika Mathur, and Gajendra PS Raghava · 2016
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Propagation kernels: efficient graph kernels from propagated information
Marion Neumann, Roman Garnett, Christian Bauckhage, and Kristian Kersting · 2016
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Diffusion-convolutional neural networks
James Atwood and Don Towsley · 2016
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Neural message passing for quantum chemistry
James Gilmer, Samuel S. Schoenholz, Patrick F. Riley, Oriol Vinyals, and George E. Dahl · 2017
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Drug delivery by supramolecular design
Matthew J Webber and Robert Langer · 2017
Open graph benchmark: Datasets for machine learning on graphs
Weihua Hu, Matthias Fey, Marinka Zitnik, Yuxiao Dong, Hongyu Ren, Bowen Liu, Michele Catasta, and Jure Leskovec · 2020
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Natural graph networks
Pim de Haan, Taco S. Cohen, and Max Welling · 2020
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Tudataset: A collection of benchmark datasets for learning with graphs
Christopher Morris, Nils M Kriege, Franka Bause, Kristian Kersting, Petra Mutzel, and Marion Neumann · 2020
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Highly accurate protein structure prediction with alphafold
John Jumper, Richard Evans, Alexander Pritzel, Tim Green, Michael Figurnov, Olaf Ronneberger, Kathryn Tunyasuvunakool, Ruth Bates, Augustin Žídek, Artur Potapenko, et al · 2021
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Neural algorithmic reasoning
Petar Veličković and Charles Blundell · 2021
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Solar cell efficiency tables (version 57)
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Inductive representation learning on large graphs
William L. Hamilton, Rex Ying, and Jure Leskovec · 2017
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Geometric deep learning on graphs and manifolds using mixture model cnns
Federico Monti, Davide Boscaini, Jonathan Masci, Emanuele Rodola, Jan Svoboda, and Michael M Bronstein · 2017
Cited alongside, same era.
Xavier Bresson and Thomas Laurent · 2017
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Relational inductive biases, deep learning, and graph networks
Peter W. Battaglia, Jessica B. Hamrick, Victor Bapst, Alvaro Sanchez-Gonzalez, Vinicius Zambaldi, Mateusz Malinowski, Andrea Tacchetti, David Raposo, Adam Santoro, Ryan Faulkner, et al · 2018
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Link prediction based on graph neural networks
Muhan Zhang and Yixin Chen · 2018
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Graph attention networks
Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Liò, and Yoshua Bengio · 2018
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Martin Green, Ewan Dunlop, Jochen Hohl-Ebinger, Masahiro Yoshita, Nikos Kopidakis, and Xiaojing Hao · 2021
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On the bottleneck of graph neural networks and its practical implications
Uri Alon and Eran Yahav · 2021
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Nested graph neural networks
Muhan Zhang and Pan Li · 2021
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A generalization of transformer networks to graphs
Vijay Prakash Dwivedi and Xavier Bresson · 2021
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Rethinking graph transformers with spectral attention
Devin Kreuzer, Dominique Beaini, Will Hamilton, Vincent Létourneau, and Prudencio Tossou · 2021
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Do transformers really perform badly for graph representation?
Chengxuan Ying, Tianle Cai, Shengjie Luo, Shuxin Zheng, Guolin Ke, Di He, Yanming Shen, and Tie-Yan Liu · 2021
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Directional graph networks
Dominique Beaini, Saro Passaro, Vincent Létourneau, William L Hamilton, Gabriele Corso, and Pietro Liò · 2021
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Supramolecular chemistry
Jonathan W Steed and Jerry L Atwood · 2022
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Improving graph neural network expressivity via subgraph isomorphism counting
Giorgos Bouritsas, Fabrizio Frasca, Stefanos Zafeiriou, and Michael Bronstein · 2022
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Equivariant subgraph aggregation networks
Beatrice Bevilacqua, Fabrizio Frasca, Derek Lim, Balasubramaniam Srinivasan, Chen Cai, Gopinath Balamurugan, Michael M Bronstein, and Haggai Maron · 2022
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From stars to subgraphs: Uplifting any gnn with local structure awareness
Lingxiao Zhao, Wei Jin, Leman Akoglu, and Neil Shah · 2022
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Understanding and extending subgraph gnns by rethinking their symmetries
Fabrizio Frasca, Beatrice Bevilacqua, Michael Bronstein, and Haggai Maron · 2022
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Your transformer may not be as powerful as you expect
Shengjie Luo, Shanda Li, Shuxin Zheng, Tie-Yan Liu, Liwei Wang, and Di He · 2022
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Long range graph benchmark
Vijay Prakash Dwivedi, Ladislav Rampášek, Michael Galkin, Ali Parviz, Guy Wolf, Anh Tuan Luu, and Dominique Beaini · 2022
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Neural sheaf diffusion: A topological perspective on heterophily and oversmoothing in GNNs
Cristian Bodnar, Francesco Di Giovanni, Benjamin Paul Chamberlain, Pietro Lio, and Michael M. Bronstein · 2022
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Surfing on the neural sheaf
Julian Suk, Lorenzo Giusti, Tamir Hemo, Miguel Lopez, Konstantinos Barmpas, and Cristian Bodnar · 2022
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Simplicial attention networks
Christopher Wei Jin Goh, Cristian Bodnar, and Pietro Liò · 2022
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Sheaf attention networks
Federico Barbero, Cristian Bodnar, Haitz Sáez de Ocáriz Borde, and Pietro Lio · 2022
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Higher-order attention networks, 2022
Mustafa Hajij, Ghada Zamzmiand, Theodore Papamarkou, Nina Miolane, Aldo Guzmàn-Sàenz, and Karthikeyan Natesan Ramamurthy · 2022
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Parallel and distributed graph neural networks: An in-depth concurrency analysis, 2022
Maciej Besta and Torsten Hoefler · 2022
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On over-squashing in message passing neural networks: The impact of width, depth, and topology
Francesco Di Giovanni, Lorenzo Giusti, Federico Barbero, Giulia Luise, Pietro Lio, and Michael Bronstein · 2023
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Rethinking the expressive power of gnns via graph biconnectivity, 2023
Bohang Zhang, Shengjie Luo, Liwei Wang, and Di He · 2023
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Architectures of topological deep learning: A survey on topological neural networks
Mathilde Papillon, Sophia Sanborn, Mustafa Hajij, and Nina Miolane · 2023
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