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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
Marco Gori: Graph Representations, Backpropagation, and Biological Plausibility (Talk)
Peter Battaglia: Graph Networks for Learning Physics (Talk)
Open Challenges - Spotlight Presentations (Spotlight)
Coffee Break (Break)
Andrew McCallum: Learning DAGs and Trees with Box Embeddings and Hyperbolic Embeddings (Talk)
Poster Session #1 (Poster Session)
Lunch (Break)
Outstanding Contribution Talk: Pre-training Graph Neural Networks (Talk)
Outstanding Contribution Talk: Variational Graph Convolutional Networks (Talk)
Outstanding Contribution Talk: Probabilistic End-to-End Graph-based Semi-Supervised Learning (Talk)
Wengong Jin: Representation and Synthesis of Molecular Graphs (Talk)
Presentation and Discussion: Open Graph Benchmark (Talk and Discussion)
Poster Session #2 (Poster Session)
Bistra Dilkina: Graph Representation Learning for Optimization on Graphs (Talk)
Marinka Zitnik: Graph Neural Networks for Drug Discovery and Development (Talk)
Invited Presentation: Deep Graph Library (Talk)