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 …

This paper studies the problem of traffic flow forecasting, which aims to predict future traffic
conditions on the basis of road networks and traffic conditions in the past. The problem is …

Motivation The crux of molecular property prediction is to generate meaningful
representations of the molecules. One promising route is to exploit the molecular graph …

Graph neural networks (GNNs) process the graph-structured data using neural networks
and have proven successful in various graph processing tasks. Currently, graph pooling …

Humans possess a versatile mechanism for extracting structured representations of our
visual world. When looking at an image, we can decompose the scene into entities and their …

Retrosynthetic planning plays an important role in the field of organic chemistry, which could
generate a synthetic route for the target product. The synthetic route is a series of reactions …

Incorporating Euclidean symmetries (eg rotation equivariance) as inductive biases into
graph neural networks has improved their generalization ability and data efficiency in …

Graph neural networks (GNNs) have demonstrated a significant success in various graph
learning tasks, from graph classification to anomaly detection. There recently has emerged a …

Graphs have been widely adopted in various fields, where many graph models are
developed. Most of previous research focuses on unipartite or homogeneous graph …

Knowledge graphs as external information has become one of the mainstream directions of
current recommendation systems. Various knowledge-graph-representation methods have …