  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 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 {\em known} and {\em fixed}. Often, this assumption is not
true since the graph may be noisy, or partially and even completely unknown. In
such cases, it would be helpful to infer the graph directly from the data,
especially in inductive settings where some nodes were not present in the graph
at training time. Furthermore, learning a graph may become an end in itself, as
the inferred structure may provide complementary insights next to the
downstream task. In this paper, we introduce Differentiable Graph Module (DGM),
a learnable function that predicts edge probabilities in the graph which are
optimal for the downstream task. DGM 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 (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.
