Date and time: Friday, 17 July 2020, 8:40AM – 7:00PM (GMT+2)
The workshop will be held virtually due to risks and travel restrictions associated with SARS-CoV-2/COVID-19. More information to follow; for more information from ICML, please see the ICML conference website.

Recent years have seen a surge in research on graph representation learning [1,2,8], including techniques for deep graph embeddings [7,12,14], generalizations of CNNs to graph-structured data [3,5,11], and neural message-passing approaches [6,9,10,16]. These advances in graph neural networks and related techniques have led to new state-of-the-art results in numerous domains: chemical synthesis [20], 3D-vision [18], recommender systems [19], question answering [15], continuous control [17], self-driving [4] and social network analysis [13].

Building on the successes of three related workshops from last year (at ICML, ICLR and NeurIPS), the primary goal for this workshop is to facilitate community building, and support expansion of graph representation learning into more interdisciplinary projects with the natural and social sciences. With hundreds of new researchers beginning projects in this area, we hope to bring them together to consolidate this fast-growing area into a healthy and vibrant subfield. Especially, we aim to strongly promote novel and exciting applications of graph representation learning across the sciences, reflected in our choices of invited speakers.

The workshop will consist of contributed talks, virtual poster sessions, and invited talks on a wide variety of methods and problems in this area, including but not limited to:

• Supervised deep learning on graphs (e.g., graph neural networks)
• Unsupervised graph embedding methods, and deep generative models of graphs
• Geometric deep learning (e.g., representation learning on manifolds, point clouds in computer vision)
• Applications of graph representation learning across the natural and social sciences
• Benchmark datasets and evaluation methods

References

[1] P. Battaglia et al. Relational inductive biases, deep learning, and graph networks. arXiv preprintarXiv:1806.01261, 2018.
[2] M.M. Bronstein, J Bruna, Y. LeCun, A. Szlam, and P. Vandergheynst. Geometric deep learning: going beyond euclidean data. IEEE Signal Processing Magazine, 34(4):18–42, 2017.
[3] Joan Bruna, Wojciech Zaremba, Arthur Szlam, and Yann LeCun. Spectral networks and locally connected networks on graphs. arXiv preprint arXiv:1312.6203, 2013.
[4] Sergio Casas, Cole Gulino, Renjie Liao, and Raquel Urtasun. Spatially-aware graph neural networks for relational behavior forecasting from sensor data. In International Conference on Robotics and Automation, 2020.
[5] Michaël Defferrard, Xavier Bresson, and Pierre Vandergheynst. Convolutional neural networks on graphs with fast localized spectral filtering. In Advances in neural information processing systems, pages 3844–3852, 2016.
[6] Justin Gilmer, Samuel S Schoenholz, Patrick F Riley, Oriol Vinyals, and George E Dahl. Neural message passing for quantum chemistry. In Proceedings of the 34th International Conference on Machine Learning-Volume 70, pages 1263–1272. JMLR. org, 2017.
[7] Aditya Grover and Jure Leskovec. node2vec: Scalable feature learning for networks. In Proceedings of the 22nd ACM SIGKDD international conference on Knowledge discovery and data mining, pages 855–864, 2016.
[8] W.L. Hamilton, R Ying, and J Leskovec. Representation learning on graphs: Methods and applications.IEEEData Engineering Bulletin, 2017.
[9] Thomas N Kipf and Max Welling. Semi-supervised classification with graph convolutional networks. arXivpreprint arXiv:1609.02907, 2016.
[10] Yujia Li, Daniel Tarlow, Marc Brockschmidt, and Richard Zemel. Gated graph sequence neural networks. arXivpreprint arXiv:1511.05493, 2015.
[11] Federico Monti, Davide Boscaini, Jonathan Masci, Emanuele Rodola, Jan Svoboda, and Michael M Bronstein. Geometric deep learning on graphs and manifolds using mixture model cnns. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 5115–5124, 2017.
[12] Bryan Perozzi, Rami Al-Rfou, and Steven Skiena. Deepwalk: Online learning of social representations. In Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining, pages 701–710, 2014.
[13] Jiezhong Qiu, Jian Tang, Hao Ma, Yuxiao Dong, Kuansan Wang, and Jie Tang. Deepinf: Social influence prediction with deep learning. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pages 2110–2119, 2018.
[14] Jian Tang, Meng Qu, Mingzhe Wang, Ming Zhang, Jun Yan, and Qiaozhu Mei. Line: Large-scale informationnetwork embedding. In Proceedings of the 24th international conference on world wide web, pages 1067–1077,2015.
[15] Damien Teney, Lingqiao Liu, and Anton van Den Hengel. Graph-structured representations for visual question answering. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 1–9,2017.
[16] Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio, and Yoshua Bengio. Graph attention networks. arXiv preprint arXiv:1710.10903, 2017.
[17] Tingwu Wang, Renjie Liao, Jimmy Ba, and Sanja Fidler. Nervenet: Learning structured policy with graph neural networks. In International Conference on Learning Representations, 2018.
[18] Yue Wang, Yongbin Sun, Ziwei Liu, Sanjay E Sarma, Michael M Bronstein, and Justin M Solomon. Dynamic graph cnn for learning on point clouds. ACM Transactions on Graphics (TOG), 38(5):1–12, 2019.
[19] Rex Ying, Ruining He, Kaifeng Chen, Pong Eksombatchai, William L Hamilton, and Jure Leskovec. Graph convolutional neural networks for web-scale recommender systems. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pages 974–983, 2018.
[20] Jiaxuan You, Bowen Liu, Zhitao Ying, Vijay Pande, and Jure Leskovec. Graph convolutional policy network for goal-directed molecular graph generation. In Advances in neural information processing systems, pages6410–6421, 2018.