Abstract Machine learning plays an increasingly important role in many areas of chemistry
and materials science, being used to predict materials properties, accelerate simulations …

Graph neural networks have emerged as a leading architecture for many graph-level tasks,
such as graph classification and graph generation. As an essential component of the …

Graph Neural Networks (GNNs), which generalize deep neural networks to graph-structured
data, have drawn considerable attention and achieved state-of-the-art performance in …

This article studies self-supervised graph representation learning, which is critical to various
tasks, such as protein property prediction. Existing methods typically aggregate …

Graph neural networks (GNNs) tend to suffer from high computation costs due to the
exponentially increasing scale of graph data and a large number of model parameters …

Graph Neural Networks (GNNs), whch generalize deep neural networks to graph-structured
data, have drawn considerable attention and achieved state-of-the-art performance in …

Graph classification is an important problem with applications across many domains, for
which graph neural networks (GNNs) have been state-of-the-art (SOTA) methods. In the …

In this article, we present a novel hierarchical bidirected graph convolution network (HiBi-
GCN) for large-scale 3-D point cloud place recognition. Unlike place recognition methods …

Representation learning is vital for the performance of multivariate time series (MTS)-related
tasks. Given high-dimensional MTS data, researchers generally rely on deep learning …

In this paper, we propose a novel pooling layer for graph neural networks based on
maximizing the mutual information between the pooled graph and the input graph. Since the …