## Contributed talks

### Original research

**Pre-training Graph Neural Networks**.*Weihua Hu, Bowen Liu, Joseph Gomes, Marinka Žitnik, Vijay S. Pande, Percy Liang and Jure Leskovec***Variational Graph Convolutional Networks**.*Louis C. Tiao, Pantelis Elinas, Harrison Tri Tue Nguyen and Edwin V. Bonilla***Probabilistic End-to-End Graph-based Semi-Supervised Learning**.*Mariana Vargas Vieyra, Aurélien Bellet and Pascal Denis*

### Open problems and challenges

**Between the Interaction of Graph Neural Networks and Semantic Web**.*Francisco Xavier Sumba Toral***Disentangling structure and position in graphs**.*Komal Teru and Will Hamilton***Approximation Power of Invariant Graph Networks**.*Haggai Maron, Heli Ben-Hamu and Yaron Lipman***Intrinsic evaluation of unsupervised node embedding**.*Chi Thang Duong, Dung Trung Hoang, Quoc Viet Hung Nguyen, Ha The Hien Dang and Karl Aberer***Leveraging Time Dependency in Graphs**.*Arinbjörn Kolbeinsson, Naman Shukla, Akhil Gupta and Lavanya Marla*

## Poster Session #1

**Node2Motif: Hierarchical Invariant Embeddings of Structured Graphs Using the Bispectrum**.*Sophia Sanborn, Ram Mehta, Noah Shutty and Christopher Hillar***Learning Hierarchical Representations in Kinematic Space**.*Adarsh Jamadandi and Uma Mudenagudi***Applying Graph Neural Networks on Heterogeneous Nodes and Edge Features**.*Frederik Diehl***Natural Question Generation with Reinforcement Learning Based Graph-to-Sequence Model**.*Yu Chen, Lingfei Wu and Mohammed Zaki***Convolution, attention and structure embedding**.*Jean-Marc Andreoli***Disentangling Interpretable Generative Parameters of Random and Real-World Graphs**.*Niklas Stoehr, Emine Yilmaz, Marc Brockschmidt and Jan Stuehmer***Graph Few-shot Learning via Knowledge Transfer**.*Huaxiu Yao, Chuxu Zhang, Ying Wei, Meng Jiang, Suhang Wang, Junzhou Huang, Nitesh Chawla and Zhenhui (Jessie) Li***Group Representation Theory for Knowledge Graph Embedding**.*Chen Cai, Yufeng Cai, Mingming Sun and Zhiqiang Xu***Graph Generation with Variational Recurrent Neural Network**.*Shih-Yang Su, Hossein Hajimirsadeghi and Greg Mori***Graph Embedding VAE: A Permutation Invariant Model of Graph Structure**.*Tony Duan and Juho Lee***Learning Visual Dynamics Models of Rigid Objects using Relational Inductive Biases**.*Fabio Ferreira, Lin Shao, Tamim Asfour and Jeannette Bohg***Image-Conditioned Graph Generation for Road Network Extraction**.*Davide Belli and Thomas Kipf***Deep geometric matrix completion: Are we doing it right?**.*Amit Boyarski, Sanketh Vendula and Alex Bronstein***Curvature Graph Network**.*Ze Ye, Kin Sum Liu, Tengfei Ma, Jie Gao and Chao Chen***Sequential Edge Clustering in Temporal Multigraphs**.*Elahe Ghalebi, Hamidreza Mahyar, Radu Grosu, Graham Taylor and Sinead Williamson***Learning interpretable hierarchical node representations via Ladder Gamma VAE**.*Arindam Sarkar, Nikhil Mehta and Piyush Rai***Multimodal Neural Graph Memory Networks for Visual Question Answering**.*Mahmoud Khademi, Parmis Naddaf and Oliver Schulte***Graph Alignment Networks with Node Matching Scores**.*Evgeniy Faerman, Otto Voggenreiter, Felix Borutta, Tobias Emrich, Max Berrendorf and Matthias Schubert***Graph Attacks with Latent Variable Noise Modeling**.*Joey Bose, Andre Cianflone and Will Hamilton***Graph Representation Learning via Multi-task Knowledge Distillation**.*Jiaqi Ma and Qiaozhu Mei***IsoNN: Isomorphic Neural Network for Graph Representation Learning and Classification**.*Lin Meng and Jiawei Zhang***Diachronic Embedding for Temporal Knowledge Graph Completion**.*Rishab Goel, Seyed Mehran Kazemi, Marcus Brubaker and Pascal Poupart***Improving Graph Attention Networks with Large Margin-based Constraints**.*Guangtao Wang, Rex Ying, Jing Huang and Jure Leskovec***Representation Learning of EHR Data via Graph-Based Medical Entity Embedding**.*Tong Wu, Yunlong Wang, Yue Wang, Emily Zhao, Yilian Yuan and Zhi Yang***Active Learning for Graph Neural Networks via Node Feature Propagation**.*Yuexin Wu, Yichong Xu, Yiming Yang and Aarti Singh***On Learning Paradigms for the Travelling Salesman Problem**.*Chaitanya K. Joshi, Thomas Laurent and Xavier Bresson***GNN-FiLM: Graph Neural Networks with Feature-wise Linear Modulation**.*Marc Brockschmidt***Graph Embeddings from Random Neural Features**.*Daniele Zambon, Cesare Alippi and Lorenzo Livi***Graph Structured Prediction Energy Net Algorithms**.*Colin Graber and Alexander Schwing***Meta-Graph: Few shot Link Prediction via Meta-Learning**.*Joey Bose, Ankit Jain, Piero Molino and Will Hamilton***Graph Representation Learning for Fraud Prediction: A Nearest Neighbour approach**.*Rafaël Van Belle, Sandra Mitrović and Jochen De Weerdt***Tensor Graph Neural Networks for Learning on Time Varying Graphs**.*Osman Asif Malik, Shashanka Ubaru, Lior Horesh, Misha E. Kilmer and Haim Avron***Learning representations of Logical Formulae using Graph Neural Networks**.*Xavier Glorot, Ankit Anand, Eser Aygün, Shibl Mourad, Pushmeet Kohli and Doina Precup***SELFIES: a robust representation of semantically constrained graphs with an example application in chemistry**.*Mario Krenn, Florian Haese, Akshat Nigam, Pascal Friederich and Alan Aspuru-Guzik***Predicting Propositional Satisfiability via End-to-End Learning**.*Chris Cameron, Rex H.-G. Chen, Jason S. Hartford and Kevin Leyton-Brown***Residual or Gate? Towards Deeper Graph Neural Networks for Inductive Graph Representation Learning**.*Binxuan Huang and Kathleen M. Carley***Contextual Parameter Generation for Knowledge Graph Link Prediction**.*George I. Stoica, Otilia Stretcu, Anthony Platanios, Tom Mitchell and Barnabas Poczos***DynGAN: Generative Adversarial Networks for Dynamic Network Embedding**.*Ayush Maheshwari, Ayush Goyal, Manjesh Kumar Hanawal and Ganesh Ramakrishnan***Relational Graph Representation Learning for Predicting Object Affordances**.*Alexia Toumpa and Anthony Cohn***Conditional Neural Style Transfer with Peer-Regularized Feature Transform**.*Jan Svoboda, Asha Anoosheh, Christian Osendorfer and Jonathan Masci***R-SQAIR: Relational Sequential Attend, Infer, Repeat**.*Aleksandar Stanić and Jürgen Schmidhuber*

## Poster Session #2

**Learning Compositional Koopman Operators for Model-Based Control**.*Yunzhu Li, Hao He, Jiajun Wu, Dina Katabi and Antonio Torralba***PiNet: Attention Pooling for Graph Classification**.*Peter Meltzer, Marcelo Gutierrez Mallea and Peter Bentley***On Node Features for Graph Neural Networks**.*Chi Thang Duong, Thanh Dat Hoang, Ha The Hien Dang, Quoc Viet Hung Nguyen and Karl Aberer***Multi-Graph Convolutional Neural Networks for Representation Learning in Recommendation**.*Jianing Sun and Yingxue Zhang***Keep It Simple: Graph Autoencoders Without Graph Convolutional Networks**.*Guillaume Salha, Romain Hennequin and Michalis Vazirgiannis***Differentiation of Black-Box Combinatorial Solvers**.*Marin Vlastelica Pogančić, Anselm Paulus, Vit Musil, Georg Martius and Michal Rolinek***Auto-regressive Graph Generation Modeling with Improved Evaluation Methods**.*Chia-Cheng Liu, Harris Chan and Kevin Luk***Policy Learning for Task-driven Discovery of Incomplete Networks**.*Peter Morales, Rajmonda Caceres, and Tina Eliassi-Rad***Learning interpretable disease self-representations for drug repositioning**.*Fabrizio Frasca, Diego Galeano, Guadalupe Gonzalez, Ivan Laponogov, Kirill Veselkov, Alberto Paccanaro and Michael Bronstein***Building Dynamic Knowledge Graphs from Text-based Games**.*Mikuláš Zelinka, Xingdi Yuan, Marc-Alexandre Côté, Romain Laroche and Adam Trischler***GraphMix: Improved Training of Graph Neural Networks for Semi-Supervised Learning**.*Vikas Verma, Alex M. Lamb, Juho Kannala, Yoshua Bengio and Jian Tang***Learning Node Embeddings with Exponential Family Distributions**.*Abdulkadir Celikkanat and Fragkiskos Malliaros***Group Anomaly Detection via Graph Autoencoders**.*Pierluca D’Oro, Ennio Nasca, Jonathan Masci and Matteo Matteucci***Network discovery using reinforcement learning**.*Harshavardhan P. Kamarthi, Priyesh Vijayan, Bryan Wilder, Balaraman Ravindran and Milind Tambe***A quantum hardware-induced graph kernel based on Gaussian Boson Sampling**.*Maria Schuld, Kamil Bradler, Robert Israel, Daiqin Su and Brajesh Gupt***Neural Execution of Graph Algorithms**.*Petar Veličković, Rex Ying, Matilde Padovano, Raia Hadsell and Charles Blundell***Short Text Classification using Graph Convolutional Network**.*Kshitij Tayal, Nikhil Rao, Karthik Subbian and Saurabh Agrawal***Logical Expressiveness of Graph Neural Networks**.*Mikaël Monet, Jorge Pérez, Juan Reutter, Egor Kostylev, Pablo Barceló and Juan Pablo Silva***Dynamic Network Representation Learning via Gaussian Embedding**.*Yulong Pei, Xin Du, George Fletcher and Mykola Pechenizkiy***Tri-graph Information Propagation for Polypharmacy Side Effect Prediction**.*Hao Xu, Shengqi Sang and Haiping Lu***Attributed Random Walk as Matrix Factorization**.*Lei Chen, Shunwang Gong, Joan Bruna and Michael Bronstein***Graph Sequential Networks**.*Ming Tu, Jing Huang, Xiaodong He and Bowen Zhou***Dynamic Embedding on Textual Networks via a Gaussian Process**.*Pengyu Cheng, Yitong Li, Xinyuan Zhang, Liqun Chen, David Carlson and Lawrence Carin***Low-Dimensional Knowledge Graph Embeddings via Hyperbolic Rotations**.*Ines Chami, Adva Wolf, Frederic Sala and Christopher Ré***Supervised Graph Attention Network for Semi-Supervised Node Classification**.*Dongkwan Kim and Alice Oh***Community detection and collaborative filtering on zero inflated graphs using spectral clustering**.*Guilherme Gomes, Vinayak Rao and Jennifer Neville***Molecule-Augmented Attention Transformer**.*Łukasz Maziarka, Tomasz Danel, Slawomir Mucha, Krzysztof Rataj, Jacek Tabor and Stanislaw Jastrzebski***Disentangling Mixtures of Epidemics on Graphs**.*Jessica Hoffmann, Soumya Basu, Surbhi Goel and Constantine Caramanis***Transferability of Spectral Graph Convolutional Neural Networks**.*Ron Levie, Wei Huang, Lorenzo Bucci, Michael Bronstein and Gitta Kutyniok***On the Interpretability and Evaluation of Graph Representation Learning**.*Antonia Gogoglou, C. Bayan Bruss and Keegan Hines***Graph Attentional Autoencoder for Anticancer Hyperfood Prediction**.*Shunwang Gong, Guadalupe Gonzalez, Ivan Laponogov, Kirill Veselkov and Michael Bronstein***Learning interaction patterns from surface representations of protein structure**.*Pablo Gainza Cirauqui, Freyr Sverrisson, Federico Monti, Emanuele Rodolà, Davide Boscaini, Michael Bronstein and Bruno Correia***Graph-Driven Generative Models for Heterogeneous Multi-Task Learning**.*Wenlin Wang, Hongteng Xu, Zhe Gan and Wenqi Wang***Observational causal inference using network information**.*Yan Leng, Martin Saveski, Alex ‘Sandy’ Pentland and Dean Eckles***Relational Graph Representation Learning for Open-Domain Question Answering**.*Salvatore G. Vivona and Kaveh Hassani***Modeling Human Brain Connectomes using Structured Neural Networks**.*Uday Shankar Shanthamallu, Qunwei Li, Jayaraman Thiagarajan, Rushil Anirudh, Alan Kaplan and Peer-Timo Bremer***Neural Message Passing on High Order Paths**.*Daniel Flam-Shepherd***Multi-Task Learning on Graphs with Node and Graph Level Labels**.*Chester Holtz, Onur Atan, Ryan Carey and Tushit Jain***Learnable Aggregator for GCN**.*Li Zhang and Haiping Lu***Deep Generative Probabilistic Graph Neural Networks for Scene Graph Generation**.*Mahmoud Khademi and Oliver Schulte***Towards an Adaptive Skip-gram Model for Network Representation Learning**.*I-Chung Hsieh and Cheng-Te Li***Understanding Graph Neural Networks via Trajectory Analysis**.*Ziqiao Meng, Jin Dong, Zengfeng Huang and Irwin King***Learning Vertex Convolutional Networks for Graph Classification**.*Yuhang Jiao, Lixin Cui, Lu Bai and Hancock Edwin*