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