DeepAI AI Chat
Log In Sign Up

Differentiable Graph Module (DGM) Graph Convolutional Networks

by   Anees Kazi, et al.

Graph deep learning has recently emerged as a powerful ML concept allowing to generalize successful deep neural architectures to non-Euclidean structured data. Such methods have shown promising results on a broad spectrum of applications ranging from social science, biomedicine, and particle physics to computer vision, graphics, and chemistry. One of the limitations of the majority of the current graph neural network architectures is that they are often restricted to the transductive setting and rely on the assumption that the underlying graph is known and fixed. In many settings, such as those arising in medical and healthcare applications, this assumption is not necessarily true since the graph may be noisy, partially- or even completely unknown, and one is thus interested in inferring it from the data. This is especially important in inductive settings when dealing with nodes not present in the graph at training time. Furthermore, sometimes such a graph itself may convey insights that are even more important than the downstream task. In this paper, we introduce Differentiable Graph Module (DGM), a learnable function predicting the edge probability in the graph relevant for the task, that can be combined with convolutional graph neural network layers and trained in an end-to-end fashion. We provide an extensive evaluation of applications from the domains of healthcare (disease prediction), brain imaging (gender and age prediction), computer graphics (3D point cloud segmentation), and computer vision (zero-shot learning). We show that our model provides a significant improvement over baselines both in transductive and inductive settings and achieves state-of-the-art results.


page 7

page 8


SIGN: Scalable Inception Graph Neural Networks

Geometric deep learning, a novel class of machine learning algorithms ex...

A Review of Graph Neural Networks and Their Applications in Power Systems

Deep neural networks have revolutionized many machine learning tasks in ...

Learning Graph Structure from Convolutional Mixtures

Machine learning frameworks such as graph neural networks typically rely...

Factor Graph Neural Network

Most of the successful deep neural network architectures are structured,...

Inductive Graph Neural Networks for Moving Object Segmentation

Moving Object Segmentation (MOS) is a challenging problem in computer vi...

From Latent Graph to Latent Topology Inference: Differentiable Cell Complex Module

Latent Graph Inference (LGI) relaxed the reliance of Graph Neural Networ...

Symmetry-driven graph neural networks

Exploiting symmetries and invariance in data is a powerful, yet not full...