| layout | title | permalink |
|---|---|---|
post |
Accepted Papers |
/papers/ |
- Get Rid of Suspended Animation: Deep Diffusive Neural Network for Graph Representation Learning. Jiawei Zhang
- Learning Graph Models for Template-Free Retrosynthesis. Vignesh Ram Somnath, Charlotte Bunne, Connor W. Coley, Andreas Krause and Regina Barzilay
- Frequent Subgraph Mining by Walking in Order Embedding Space. Rex Ying, Andrew Z. Wang, Jiaxuan You and Jure Leskovec
- Wiki-CS: A Wikipedia-Based Benchmark for Graph Neural Networks. Péter Mernyei and Cătălina Cangea
- Graph Neural Networks for Massive MIMO Detection. Andrea Scotti, Nima N. Moghadam, Dong Liu, Karl Gafvert and Jinliang Huang
- Embedding a random graph via GNN: Extended mean-field inference theory and RL applications to NP-Hard multi-robot/machine scheduling. Hyunwook Kang, Aydar Mynbay, James R. Morrison* and Jinkyoo Park*
- Navigating the Dynamics of Financial Embeddings over Time. Antonia Gogoglou, Brian Nguyen, Alan Salimov, Jonathan B. Rider and C. Bayan Bruss
- Integrating Logical Rules Into Neural Multi-Hop Reasoning for Drug Repurposing. Yushan Liu*, Marcel Hildebrandt*, Mitchell Joblin, Martin Ringsquandl and Volker Tresp
- Gaining insight into SARS-CoV-2 infection and COVID-19 severity using self-supervised edge features and Graph Neural Networks. Arijit Sehanobish*, Neal Ravindra* and David van Dijk
Best paper award: Gaining insight into SARS-CoV-2 infection and COVID-19 severity using self-supervised edge features and Graph Neural Networks
- When Spectral Domain Meets Spatial Domain in Graph Neural Networks. Muhammet Balcilar, Guillaume Renton, Pierre Héroux, Benoit Gaüzère, Sébastien Adam and Paul Honeine
- Graph Neural Networks in TensorFlow and Keras with Spektral. Daniele Grattarola and Cesare Alippi
- Deep Graph Contrastive Representation Learning. Yanqiao Zhu, Yichen Xu, Feng Yu, Qiang Liu, Shu Wu and Liang Wang
- Hierarchical Protein Function Prediction with Tail-GNNs. Stefan Spalević, Petar Veličković, Jovana Kovačević and Mladen Nikolić
- Neural Bipartite Matching. Dobrik Georgiev and Pietro Liò
- Principal Neighbourhood Aggregation for Graph Nets. Gabriele Corso*, Luca Cavalleri*, Dominique Beaini, Pietro Liò and Petar Veličković
- Contrastive Graph Neural Network Explanation. Lukas Faber*, Amin K. Moghaddam* and Roger Wattenhofer*
- Set2Graph: Learning Graphs From Sets. Hadar Serviansky, Nimrod Segol, Jonathan Shlomi, Kyle Cranmer, Eilam Gross, Haggai Maron and Yaron Lipman
- A Graph VAE and Graph Transformer Approach to Generating Molecular Graphs. Joshua Mitton, Hans M. Senn, Klaas Wynne and Roderick Murray-Smith
- Geometric Matrix Completion: A Functional View. Abhishek Sharma and Maks Ovsjanikov
- Hierarchically Attentive Graph Pooling with Subgraph Attention. Sambaran Bandyopadhyay, Manasvi Aggarwal and M. Narasimha Murty
- Hierarchical Inter-Message Passing for Learning on Molecular Graphs. Matthias Fey*, Jan-Gin Yuen* and Frank Weichert
- Graph Neural Networks for Massive MIMO Detection. Andrea Scotti, Nima N. Moghadam, Dong Liu, Karl Gafvert and Jinliang Huang
- Graph Convolutional Gaussian Processes for Link Prediction. Felix L. Opolka and Pietro Liò
- Deep Lagrangian Propagation in Graph Neural Networks. Matteo Tiezzi, Giuseppe Marra, Stefano Melacci and Marco Maggini
- Scene Graph Reasoning for Visual Question Answering. Marcel Hildebrandt*, Hang Li*, Rajat Koner*, Volker Tresp and Stephan Günnemann
- Uncertainty in Neural Relational Inference Trajectory Reconstruction. Vasileios Karavias, Ben Day and Pietro Liò
- Temporal Graph Networks for Deep Learning on Dynamic Graphs. Emanuele Rossi, Ben Chamberlain, Fabrizio Frasca, Davide Eynard, Federico Monti and Michael Bronstein
- Integrating Logical Rules Into Neural Multi-Hop Reasoning for Drug Repurposing. Yushan Liu*, Marcel Hildebrandt*, Mitchell Joblin, Martin Ringsquandl and Volker Tresp
- Differentiable Graph Module (DGM) for Graph Convolutional Networks. Anees Kazi*, Luca Cosmo*, Seyed-Ahmad Ahmadi, Nassir Navab and Michael Bronstein
- Population Graph GNNs for Brain Age Prediction. Kamilė Stankevičiūtė, Tiago Azevedo, Alexander Campbell, Richard Bethlehem and Pietro Liò
- Improving Graph Neural Network Expressivity via Subgraph Isomorphism Counting. Giorgos Bouritsas, Fabrizio Frasca, Stefanos Zafeiriou and Michael M. Bronstein
- Graph Clustering with Graph Neural Networks. Anton Tsitsulin, John Palowitch, Bryan Perozzi and Emmanuel Müller
- SIGN: Scalable Inception Graph Neural Networks. Fabrizio Frasca*, Emanuele Rossi*, Davide Eynard, Benjamin Chamberlain, Michael Bronstein and Federico Monti
- TUDataset: A collection of benchmark datasets for learning with graphs. Christopher Morris, Nils M. Kriege, Franka Bause, Kristian Kersting, Petra Mutzel and Marion Neumann
- Graphs, Entities, and Step Mixture. Kyuyong Shin, Wonyoung Shin, Jung-Woo Ha and Sunyoung Kwon
- Pointer Graph Networks. Petar Veličković, Lars Buesing, Matthew C. Overlan, Razvan Pascanu, Oriol Vinyals and Charles Blundell
- From Graph Low-Rank Global Attention to 2-FWL Approximation. Omri Puny, Heli Ben-Hamu and Yaron Lipman
- Geoopt: Riemannian Optimization in PyTorch. Max Kochurov, Rasul Karimov and Serge Kozlukov
- Active Learning on Graphs via Meta Learning. Kaushalya Madhawa and Tsuyoshi Murata
- Clustered Dynamic Graph CNN for Biometric 3D Hand Shape Recognition. Jan Svoboda, Pietro Astolfi, Davide Boscaini, Jonathan Masci and Michael Bronstein
- Graph neural induction of value iteration. Andreea Deac, Pierre-Luc Bacon and Jian Tang
- Spectral-designed Depthwise Separable Graph Neural Networks. Muhammet Balcilar, Guillaume Renton, Pierre Héroux, Benoit Gaüzère, Sébastien Adam and Paul Honeine
- Practical Adversarial Attacks on Graph Neural Networks. Jiaqi Ma*, Shuangrui Ding* and Qiaozhu Mei
- Multi-Graph Neural Operator for Parametric Partial Differential Equations. Zongyi Li, Nikola Kovachki, Kamyar Azizzadenesheli, Burigede Liu, Andrew Stuart, Kaushik Bhattacharya and Animashree Anandkumar
- Learning Distributed Representations of Graphs with Geo2DR. Paul Scherer and Pietro Liò
- Graph Neural Networks for the Prediction of Substrate-Specific Organic Reaction Conditions. Serim Ryou*, Michael R. Maser*, Alexander Y. Cui*, Travis J. DeLano, Yisong Yue and Sarah E. Reisman
- A Note on Over-Smoothing for Graph Neural Networks. Chen Cai and Yusu Wang
- Degree-Quant: Quantization-Aware Training for Graph Neural Networks. Shyam A. Tailor*, Javier Fernandez-Marques* and Nicholas D. Lane
- Message Passing Query Embedding. Daniel Daza and Michael Cochez
- Learning Graph Structure With A Finite-State Automaton Layer. Daniel Johnson, Hugo Larochelle and Daniel Tarlow
- Few-shot link prediction via graph neural networks for Covid-19 drug-repurposing. Vassilis N. Ioannidis, Da Zheng and George Karypis
- Navigating the Dynamics of Financial Embeddings over Time. Antonia Gogoglou, Brian Nguyen, Alan Salimov, Jonathan B. Rider and C. Bayan Bruss
- Generalized Multi-Relational Graph Convolution Network. Donghan Yu, Yiming Yang, Ruohong Zhang and Yuexin Wu
- Wiki-CS: A Wikipedia-Based Benchmark for Graph Neural Networks. Péter Mernyei and Cătălina Cangea
- Design Space for Graph Neural Networks. Jiaxuan You, Rex Ying and Jure Leskovec
- Meta-Learning GNN Initializations for Low-Resource Molecular Property Prediction. Cuong Q. Nguyen, Constantine Kreatsoulas and Kim M. Branson
- HNHN: Hypergraph Networks with Hyperedge Neurons. Yihe Dong, Will Sawin and Yoshua Bengio
- Uncovering the Folding Landscape of RNA Secondary Structure with Deep Graph Embeddings. Egbert Castro, Andrew Benz, Alexander Tong, Guy Wolf* and Smita Krishnaswamy*
- Discrete Planning with End-to-end Trained Neuro-algorithmic Policies. Marin Vlastelica, Michal Rolínek and Georg Martius
- SE(3)-Transformers: 3D Roto-Translation Equivariant Attention Networks. Fabian B. Fuchs*, Daniel E. Worrall*, Volker Fischer and Max Welling
- Get Rid of Suspended Animation: Deep Diffusive Neural Network for Graph Representation Learning. Jiawei Zhang
- Learning Graph Models for Template-Free Retrosynthesis. Vignesh Ram Somnath, Charlotte Bunne, Connor W. Coley, Andreas Krause and Regina Barzilay
- Relate and Predict: Structure-Aware Prediction with Jointly Optimized Neural Dependency Graph. Arshdeep Sekhon, Zhe Wang and Yanjun Qi
- Stay Positive: Knowledge Graph Embedding Without Negative Sampling. Ainaz Hajimoradlou and Seyed Mehran Kazemi
- Software Engineering Event Modeling using Relative Time in Temporal Knowledge Graphs. Kian Ahrabian, Daniel Tarlow, Hehuimin Cheng and Jin L. C. Guo
- Bi-Level Graph Neural Networks for Drug-Drug Interaction Prediction. Yunsheng Bai*, Ken Gu*, Yizhou Sun and Wei Wang
- Continuous Graph Flow. Zhiwei Deng*, Megha Nawhal*, Lili Meng and Greg Mori
- Gaining insight into SARS-CoV-2 infection and COVID-19 severity using self-supervised edge features and Graph Neural Networks. Arijit Sehanobish*, Neal Ravindra* and David van Dijk
- Molecule Edit Graph Attention Network: Modeling Chemical Reactions as Sequences of Graph Edits. Mikołaj Sacha, Mikołaj Błaż, Piotr Byrski, Paweł Włodzarczyk-Pruszyński and Stanisław Jastrzębski
- Evaluating Logical Generalization in Graph Neural Networks. Koustuv Sinha, Shagun Sodhani, Joelle Pineau and William L. Hamilton
- Relation-Dependent Sampling for Multi-Relational Link Prediction. Arthur Feeney*, Rishabh Gupta*, Veronika Thost, Rico Angell, Gayathri Chandu, Yash Adhikari and Tengfei Ma
- Frequent Subgraph Mining by Walking in Order Embedding Space. Rex Ying, Andrew Z. Wang, Jiaxuan You and Jure Leskovec
- Weisfeiler and Leman go sparse: Towards scalable higher-order graph embeddings. Christopher Morris, Gautav Rattan and Petra Mutzel
- Scattering GCN: Overcoming Oversmoothness in Graph Convolutional Networks. Yimeng Min*, Frederik Wenkel* and Guy Wolf
- Connecting Graph Convolutional Networks and Graph-Regularized PCA. Lingxiao Zhao and Leman Akoglu
- UniKER: A Unified Framework for Combining Embedding and Horn Rules for Knowledge Graph Inference. Kewei Cheng, Ziqing Yang, Ming Zhang and Yizhou Sun
- Graph Generation with Energy-Based Models. Jenny Liu, Will Grathwohl, Jimmy Ba and Kevin Swersky
- Bi-Level Attention Neural Architectures for Relational Data. Roshni G. Iyer, Wei Wang and Yizhou Sun
- Embedding a random graph via GNN: Extended mean-field inference theory and RL applications to NP-Hard multi-robot/machine scheduling. Hyunwook Kang, Aydar Mynbay, James R. Morrison* and Jinkyoo Park*
- Are Hyperbolic Representations in Graphs Created Equal?. Max Kochurov, Sergey Ivanov and Eugeny Burnaev
- Graphein - a Python Library for Geometric Deep Learning and Network Analysis on Protein Structures. Arian R. Jamasb, Pietro Liò and Tom L. Blundell
- GraphNets with Spectral Message Passing. Kimberly L. Stachenfeld, Jonathan Godwin and Peter W. Battaglia