NTU Graph Deep Learning Lab


We investigate fundamental techniques in Graph Deep Learning, a new framework that combines graph theory and deep neural networks to tackle complex data domains in physical science, natural language processing, computer vision, and combinatorial optimization.

Publications

Projects

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All Models Applications

Benchmarking Graph Neural Networks

Identify universal building blocks for robust and scalable GNNs.

Free-hand Sketches

Representation learning for drawings via graphs with geometric and temporal information.

Combinatorial Optimization

Scalable deep learning systems for practical NP-Hard combinatorial problems such as the TSP.

Quantum Chemistry

Chemical synthesis, structure and property prediction using deep neural networks.

Spatial Graph ConvNets

Graph Neural Network architectures for inductive representation learning on arbitrary graphs.

Recent Publications

Quickly discover relevant content by filtering publications.

Learning TSP Requires Rethinking Generalization

End-to-end training of neural network solvers for combinatorial problems such as the Travelling Salesman Problem is intractable and …
PDF Code Project

Benchmarking Graph Neural Networks

Graph neural networks (GNNs) have become the standard toolkit for analyzing and learning from data on graphs. As the field grows, it …
PDF Code Project Project Blog

Multi-Graph Transformer for Free-Hand Sketch Recognition

Learning meaningful representations of free-hand sketches remains a challenging task given the signal sparsity and the high-level …
PDF Code Project Blog

A Two-Step Graph Convolutional Decoder for Molecule Generation

We propose a simple auto-encoder framework for molecule generation. The molecular graph is first encoded into a continuous latent …
PDF Project Slides Video

On Learning Paradigms for the Travelling Salesman Problem

We explore the impact of learning paradigms on training deep neural networks for the Travelling Salesman Problem. We design controlled …
PDF Code Project Poster Slides
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Recent Blogposts

Benchmarking Graph Neural Networks

This blog is based on the paper Benchmarking Graph Neural Networks which is a joint work with Chaitanya K. Joshi, Thomas Laurent, …
Last updated on Jun 29, 2020

Transformers are Graph Neural Networks

Engineer friends often ask me: Graph Deep Learning sounds great, but are there any big commercial success stories? Is it being deployed …
Last updated on Mar 27, 2020

Recent Talks

Graph Neural Networks for the Travelling Salesman Problem

The most famous NP-hard combinatorial problem today, the Travelling Salesman Problem, is intractable to solve optimally at large scale. …
Oct 22, 2019 12:00 AM INFORMS Annual Meeting 2019
Project Poster Slides

Graph Convolutional Neural Networks for Molecule Generation

In this talk, I will discuss a graph convolutional neural network architecture for the molecule generation task. The proposed approach …
Sep 23, 2019 12:00 AM Institute for Pure and Applied Mathematics, UCLA
Project Slides Video

Graph Convolutional Neural Networks for Molecule Generation and Travelling Salesman Problem

In this talk, I will discuss how to apply graph convolutional neural networks to quantum chemistry and operational research. The same …
May 21, 2019 12:00 AM Institute for Pure and Applied Mathematics, UCLA
Project Project Slides Video
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People

Principal Investigator

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Xavier Bresson

Assoc. Professor of Computer Science

PhD Students

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Vijay Prakash Dwivedi

PhD Student

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David Low Jia Wei

PhD Student

Postdoctoral Scholars

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Peng Xu

Postdoctoral Scholar

Research Assistants

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Chaitanya Joshi

Research Assistant

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Victor Getty

Research Assistant

Visitors

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Axel Nilsson

Visiting Student

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