## Contributed Talks

### Original Research

**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*

### Novel Applications

**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**

### COVID-19 Applications

**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*

## Poster Session #1 (10:30–11:30AM GMT+2)

**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****Few-shot link prediction via graph neural networks for Covid-19 drug-repurposing**.*Vassilis N. Ioannidis, Da Zheng and George Karypis***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*

## Poster Session #2 (5:45–6:45PM GMT+2)

**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***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*