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Fri Dec 13 08:00 AM -- 06:00 PM (PST) @ West Exhibition Hall A
Graph Representation Learning
Will Hamilton · Rianne van den Berg · Michael Bronstein · Stefanie Jegelka · Thomas Kipf · Jure Leskovec · Renjie Liao · Yizhou Sun · Petar Veličković

Workshop Home Page

Graph-structured data is ubiquitous throughout the natural and social sciences, from telecommunication networks to quantum chemistry. Building relational inductive biases into deep learning architectures is crucial if we want systems that can learn, reason, and generalize from this kind of data. Furthermore, graphs can be seen as a natural generalization of simpler kinds of structured data (such as images), and therefore, they represent a natural avenue for the next breakthroughs in machine learning.

Recent years have seen a surge in research on graph representation learning, including techniques for deep graph embeddings, generalizations of convolutional neural networks to graph-structured data, and neural message-passing approaches inspired by belief propagation. These advances in graph neural networks and related techniques have led to new state-of-the-art results in numerous domains, including chemical synthesis, 3D-vision, recommender systems, question answering, and social network analysis.

The workshop will consist of contributed talks, contributed posters, and invited talks on a wide variety of methods and problems related to graph representation learning. We will welcome 4-page original research papers on work that has not previously been published in a machine learning conference or workshop. In addition to traditional research paper submissions, we will also welcome 1-page submissions describing open problems and challenges in the domain of graph representation learning. These open problems will be presented as short talks (5-10 minutes) immediately preceding a coffee break to facilitate and spark discussions.

The primary goal for this workshop is to facilitate community building; with hundreds of new researchers beginning projects in this area, we hope to bring them together to consolidate this fast-growing area of graph representation learning into a healthy and vibrant subfield.

Opening remarks
Will Hamilton
Marco Gori: Graph Representations, Backpropagation, and Biological Plausibility (Talk)
Marco Gori
Peter Battaglia: Graph Networks for Learning Physics (Talk)
Peter Battaglia
Open Challenges - Spotlight Presentations (Spotlight)
cuent Sumba Toral, Haggai Maron, Arinbjörn Kolbeinsson
Coffee Break (Break)
Andrew McCallum: Learning DAGs and Trees with Box Embeddings and Hyperbolic Embeddings (Talk)
Andrew McCallum
Poster Session #1 (Poster Session)
Adarsh Jamadandi, Sophia Sanborn, Huaxiu Yao, Chen Cai, Yu Chen, Jean-Marc Andreoli, Niklas Stoehr, Shih-Yang Su, Tony Duan, Fabio Ferreira, Davide Belli, Amit Boyarski, Zack Ye, Elahe Ghalebi, Arindam Sarkar, MAHMOUD KHADEMI, Evgeniy Faerman, Joey Bose, Jiaqi Ma, Lin Meng, Seyed Mehran Kazemi, Guangtao Wang, Tong Wu, Yuexin Wu, Chaitanya K Joshi, Marc Brockschmidt, Daniele Zambon, Colin Graber, Rafaël Van Belle, Osman Malik, Xavier Glorot, Mario Krenn, Christopher Cameron, Binxuan Huang, George Stoica, Alexia Toumpa
Lunch (Break)
Outstanding Contribution Talk: Pre-training Graph Neural Networks (Talk)
Bowen Liu
Outstanding Contribution Talk: Variational Graph Convolutional Networks (Talk)
Edwin Bonilla
Outstanding Contribution Talk: Probabilistic End-to-End Graph-based Semi-Supervised Learning (Talk)
mariana vargas vieyra
Wengong Jin: Representation and Synthesis of Molecular Graphs (Talk)
Wengong Jin
Presentation and Discussion: Open Graph Benchmark (Talk and Discussion)
Jure Leskovec
Poster Session #2 (Poster Session)
Yunzhu Li, Pete Meltzer, Jianing Sun, Guillaume SALHA, Marin Vlastelica Pogančić, Chia-Cheng Liu, Fabrizio Frasca, Marc-Alexandre Côté, Vikas Verma, Abdulkadir CELIKKANAT, Pierluca D'Oro, Priyesh Vijayan, Maria Schuld, Petar Veličković, Kshitij Tayal, Yulong Pei, Hao Xu, Lei Chen, Pengyu Cheng, Ines Chami, Dongkwan Kim, Guilherme Gomes, Lukasz Maziarka, Jessica Hoffmann, Ron Levie, Antonia Gogoglou, Shunwang Gong, Federico Monti, Wenlin Wang, Yan Leng, Salvatore Vivona, Daniel Flam-Shepherd, Chester Holtz, Li Zhang, MAHMOUD KHADEMI, I-Chung Hsieh, Aleksandar Stanić, Ziqiao Meng, Yuhang Jiao
Bistra Dilkina: Graph Representation Learning for Optimization on Graphs (Talk)
Bistra Dilkina
Marinka Zitnik: Graph Neural Networks for Drug Discovery and Development (Talk)
Marinka Zitnik
Invited Presentation: Deep Graph Library (Talk)
Zheng Zhang