Misc,
Differentiable Graph Module (DGM) for Graph Convolutional Networks
(2020)cite arxiv:2002.04999.
Abstract
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.
