Contributed talks & Poster presentations
Poster presentations
Morning session (10:30–11:30AM)
- A Deep Learning Framework for Graph Partitioning. Azade Nazi, Will Hang, Anna Goldie, Sujith Ravi and Azalia Mirhoesini
- Differentiable Physics-informed Graph Networks. Sungyong Seo and Yan Liu
- Advancing GraphSAGE with A Data-driven Node Sampling. Jihun Oh, Kyunghyun Cho and Joan Bruna
- Dismantle Large Networks through Deep Reinforcement Learning. Changjun Fan, Yizhou Sun, Li Zeng, Yang-Yu Liu, Muhao Chen and Zhong Liu
- Geometric Scattering for Graph Data Analysis. Feng Gao, Guy Wolf and Matthew Hirn
- ProDyn0: Inferring calponin domain stretching behavior using graph neural networks. Ali Madani, Cyna Shirazinejad, Hengameh Shams and Mohammad Mofrad
- Spatio-Temporal Deep Graph Infomax. Felix L. Opolka*, Aaron Solomon*, Cătălina Cangea, Petar Veličković, Pietro Liò and R Devon Hjelm
- Deep Graph Library: Towards Efficient And Scalable Deep Learning on Graphs. Minjie Wang, Lingfan Yu, Da Zheng, Quan Gan, Yu Gai, Zihao Ye, Mufei Li, Jinjing Zhou, Qi Huang, Chao Ma, Ziyue Huang, Qipeng Guo, Hao Zhang, Haibin Lin, Junbo Zhao, Jinyang Li, Alexander Smola and Zheng Zhang
- Learning anisotropic filters on product graphs. Clément Vignac and Pascal Frossard
- Graph Spectral Regularization For Neural Network Interpretability. Alexander Tong*, David van Dijk*, Jay S. Stanley III, Matthew Amodio, Kristina Yim, Rebecca Muhle, James Noonan, Guy Wolf and Smita Krishnaswamy
- Supervised Learning on Relational Databases with Graph Neural Networks. Milan Cvitkovic
- Clique Pooling for Graph Classification. Enxhell Luzhnica*, Ben Day* and Pietro Liò
- Comparing and Detecting Adversarial Attacks for Graph Deep Learning. Yingxue Zhang*, Sakif Hossain Khan* and Mark Coates
- SAGE: Scalable Attributed Graph Embeddings for Graph Classification. Lingfei Wu*, Zhen Zhang*, Fangli Xu, Liang Zhao and Arye Nehorai
- A Statistical Characterization of Attentions in Graph Neural Networks. Mufei Li, Hao Zhang, Xingjian Shi, Minjie Wang and Zheng Zhang
- Graph Feature Networks. Ting Chen, Song Bian and Yizhou Sun
- Bayesian Graph Convolutional Neural Networks using Non-parametric Graph Learning. Soumyasundar Pal, Florence Regol and Mark Coates
- Learning to Represent & Generate Meshes with Spiral Convolutions. Sergiy Bokhnyak*, Giorgos Bouritsas*, Michael M. Bronstein and Stefanos Zafeiriou
- SegTree Transformer: Iterative Refinement of Hierarchical Features. Zihao Ye, Qipeng Guo, Quan Gan and Zheng Zhang
- Recurrent Event Network for Reasoning over Temporal Knowledge Graphs. Woojeong Jin, Changlin Zhang, Pedro Szekely and Xiang Ren
- Graph-based Knowledge Tracing: Modeling Student Proficiency Using Graph Neural Network. Hiromi Nakagawa, Yusuke Iwasawa and Yutaka Matsuo
- DeepSphere: a graph-based spherical CNN with approximate equivariance. Michaël Defferrard, Nathanaël Perraudin, Tomasz Kacprzak and Raphaël Sgier
- Structural Node Embeddings in Graphs via Anonymous Walks. Alfredo De la Fuente and Maxim Panov
- Unsupervised Pre-training of Graph Convolutional Neural Networks. Ziniu Hu, Changjun Fan, Ting Chen, Kai-Wei Chang and Yizhou Sun
Afternoon session (4:15–5:15PM)
- Just Jump: Dynamic Neighborhood Aggregation in Graph Neural Networks. Matthias Fey
- A simple yet effective baseline for non-attributed graph classification. Chen Cai and Yusu Wang
- Tactile Graphs for Grasp Stability Prediction. Bryan S. Zapata-Impata*, Alberto Garcia-Garcia*, Sergio Orts-Escolano, Jose Garcia and Pablo Gil
- Variational Recurrent Neural Networks for Graph Classification. Edouard Pineau and Nathan de Lara
- Graph Convolutional Networks as Reward Shaping Functions. Martin Klissarov and Doina Precup
- Unsupervised Inductive Whole-Graph Embedding by Preserving Graph Proximity. Yunsheng Bai, Hao Ding, Yang Qiao, Agustin Marinovic, Ken Gu, Ting Chen, Yizhou Sun and Wei Wang
- Encoding Knowledge Graph with Graph CNN for Question Answering. Leo Laugier*, Anran Wang*, Chuan-Sheng Foo, Theo Guenais and Vijay Chandrasekhar
- Constant Time Graph Neural Networks. Ryoma Sato, Makoto Yamada and Hisashi Kashima
- Image Denoising with Graph-Convolutional Neural Networks. Diego Valsesia, Giulia Fracastoro and Enrico Magli
- Inferring Javascript types using Graph Neural Networks. Jessica V. Schrouff, Kai Wohlfahrt, Bruno Marnette and Liam Atkinson
- Can Graph Neural Networks Go “Online”? An Analysis of Pretraining and Inference. Lukas Galke, Iacopo Vagliano and Ansgar Scherp
- Conditional labeled graph generation with GANs. Shuangfei Fan and Bert Huang
- edGNN: A Simple and Powerful GNN for Directed Labeled Graphs. Guillaume Jaume*, An-phi Nguyen*, María Rodríguez Martínez, Jean-Philippe Thiran and Maria Gabrani
- GraphTSNE: A Visualization Technique For Graph-Structured Data. Yao Yang Leow, Thomas Laurent and Xavier Bresson
- Dynamic Graph Representation Learning via Self-Attention Networks. Aravind Sankar, Yanhong Wu, Liang Gou, Wei Zhang and Hao Yang
- Simulating Execution Time of Tensor Programs using Graph Neural Networks. Jakub M. Tomczak*, Romain Lepert* and Auke Wiggers*
- Molecular Geometry Prediction using a Deep Generative Graph Neural Network. Elman Mansimov, Omar Mahmood, Seokho Kang and Kyunghyun Cho
- Learning Geometric Operators on Meshes. Yu Wang, Vladimir Kim, Michael M. Bronstein and Justin Solomon
- AlChemy: A Quantum Chemistry Dataset for Benchmarking AI Models. Guangyong Chen*, Chang-Yu (Kim) Hsieh*, Chee-Kong Lee*, Ben Ben Liao*, Jiezhong Qiu*, Qiming Sun*, Jie Tang* and Shengyu Zhang*
- Graph Structure Learning for GCNs. Luca Franceschi, Mathias Niepert, Massimilliano Pontil and Xiao He
- Fake News Detection on Social Media using Geometric Deep Learning. Federico Monti, Fabrizio Frasca, Davide Eynard, Damon Mannion and Michael M. Bronstein
- Unsupervised Community Detection with Modularity-Based Attention Model. Ivan Lobov and Sergey Ivanov
- On the Use of ArXiv as a Dataset. Colin B. Clement, Matthew Bierbaum, Kevin O’Keeffe and Alexander A. Alemi