All talk recordings are now available. You can find the links in the schedule.

Accepted Papers

All accepted papers will be presented as posters during the workshop. Additionally, a small number of accepted papers was selected to be presented as contributed or spotlight talks.

Poster instructions: Posters should be roughly 24" x 36" in portrait orientation. Papers #1-#20 will be presented in the morning poster session (10:00-11:00AM) and papers #21-#40 will be presented in the afternoon poster session (3:30-4:30PM). Please remove your posters during the lunch break if you are presenting in the morning session.

List of accepted papers:

  1. Neural Message Passing for Visual Relationship Detection. Yue Hu, Siheng Chen, Xu Chen, Ya Zhang and Xiao Gu. (Spotlight)
  2. Interpretable node embeddings with mincut loss. Chi Thang Duong, Hung Nguyen Quoc Viet and Karl Aberer.
  3. Batch Virtual Adversarial Training for Graph Convolutional Networks. Zhijie Deng, Yinpeng Dong and Jun Zhu.
  4. Supervised Segmentation with Graph-Structured Deep Metric Learning. Loic Landrieu and Mohamed Boussaha.
  5. IPC: A Benchmark Data Set for Learning with Graph-Structured Data. Patrick Ferber, Tengfei Ma, Siyu Huo, Jie Chen and Michael Katz.
  6. Decoding Molecular Graph Embeddings with Reinforcement Learning. Steven Kearnes, Li Li and Patrick Riley
  7. Graph Learning Networks. Vedran J. Hadziosmanovic, Yongbin Li, Xiao Liu, Stuart Kim, David Dynerman and Loic Royer. (Spotlight)
  8. Evolutionary Representation Learning for Dynamic Graphs. Aynaz Taheri and Tanya Berger-Wolf. (Oral)
  9. Graph Learning Network: A Structure Learning Algorithm. Darwin D. Saire Pilco and Adín Ramírez Rivera. (Spotlight)
  10. Parallel Cut Pursuit For Minimization of the Graph Total-Variation. Hugo Raguet and Loic Landrieu.
  11. Graph Neural Reasoning for 2-Quantified Boolean Formula Solver. Zhanfu Yang, Fei Wang, Ziliang Chen, Guannan Wei and Tiark Rompf.
  12. Variational inference for neural network matrix factorization and its application to stochastic blockmodeling. Onno P. Kampman and Creighton Heaukulani.
  13. GraphFlow: Exploiting Conversation Flow with Graph Neural Networks for Conversational Machine Comprehension. Yu Chen, Lingfei Wu and Mohammed Zaki.
  14. Prototype Propagation Networks for Weakly-supervised Few-shot Learning on Category Graph. Lu Liu, Tianyi Zhou, Guodong Long and Jing Jiang.
  15. Compositional Structure Learning for Sequential Video Data. Kyoung-Woon On, Eun-Sol Kim, Yu-Jung Heo and Byoung-Tak Zhang.
  16. Invariant Embedding for Graph Classification. Alexis Galland and Marc Lelarge.
  17. Towards Graph Pooling by Edge Contraction. Frederik Diehl, Thomas Brunner, Michael Truong Le and Alois Knoll.
  18. Bayesian Graph Convolutional Neural Networks using Node Copying. Soumyasundar Pal, Florence Regol and Mark Coates.
  19. End-to-end learning and optimization on graphs. Bryan Wilder, Bistra Dilkina and Milind Tambe.
  20. Unsupervised extraction of interpretable graph representations from multiple-object scenes. Duo Wang, Mateja Jamnik and Pietro Liò.
  21. COMBO: Combinatorial Bayesian Optimization using Graph Representations. Changyong Oh, Jakub M. Tomczak, Efstratios Gavves and Max Welling. (Spotlight)
  22. Structure-informed Graph Auto-encoder for Relational Inference and Simulation. Yaguang Li, Chuizheng Meng, Cyrus Shahabi and Yan Liu.
  23. Factorised Neural Relational Inference for Multi-Interaction Systems. Ezra Webb, Ben J. Day, Helena Andres Terre and Pietro Lió.
  24. Are Graph Neural Networks Miscalibrated? Leonardo Teixeira, Brian Jalaian and Bruno Ribeiro. (Spotlight)
  25. Explainability Techniques for Graph Convolutional Networks. Federico Baldassarre and Hossein Azizpour. (Spotlight)
  26. Learning to Reason Mathematically. Arash Mehrjou, Ryota Tomioka, Andrew W. Fitzgibbon and Simon Pyton Jones.
  27. Privacy Preserving Adjacency Spectral Embedding on Stochastic Blockmodels. Li Chen.
  28. PAN: Path Integral Based Convolution for Deep Graph Neural Networks. Zheng Ma. (Oral)
  29. Learning Transferable Cooperative Behavior in Multi-Agent Teams. Akshat Agarwal, Sumit Kumar and Katia Sycara. (Spotlight)
  30. Deep Q-Learning for Directed Acyclic Graph Generation. Laura D'Arcy, Padraig Corcoran and Alun Preece.
  31. Question Answering by Reasoning Across Documents with Graph Convolutional Networks. Nicola De Cao, Wilker Aziz and Ivan Titov. (Spotlight)
  32. Multiple instance learning with graph neural networks. Ming Tu, Jing Huang, Xiaodong He and Bowen Zhou.
  33. Sparse Representation Classification via Screening for Graphs. Cencheng Shen, Li Chen, Yuexiao Dong and Carey E. Priebe.
  34. Tensor-based Method for Temporal Geopolitical Event Forecasting. Mehrnoosh Mirtaheri, Sami Abu-El-Haija, KSM Tozammel Hossain, Fred Morstatter and Aram Galstyan.
  35. Latent Adversarial Training of Graph Convolution Networks. Hongwei Jin and Xinhua Zhang.
  36. Towards Permutation-Invariant Graph Generation. Jenny Liu, Aviral Kumar, Jimmy Ba and Kevin Swersky.
  37. Path-Augmented Graph Transformer Network. Benson Chen, Regina Barzilay and Tommi Jaakkola. (Spotlight)
  38. Learning Graphical Structure of Electronic Health Records with Transformer for Predictive Healthcare. Edward Choi, Mike W. Dusenberry, Gerardo Flores, Zhen Xu, Yujia Li, Yuan Xue and Andrew M. Dai.
  39. On Graph Classification Networks, Datasets and Baselines. Enxhell Luzhnica, Ben Day and Pietro Lió. (Oral)
  40. Can Neural Networks Learn Symbolic Rewriting? Bartosz Piotrowski.