Graph representation learning aims to effectively encode high-dimensional sparse graph-
structured data into low-dimensional dense vectors, which is a fundamental task that has …

Self-supervised graph representation learning has recently shown considerable promise in
a range of fields, including bioinformatics and social networks. A large number of graph …

This paper investigates traffic forecasting, which attempts to forecast the future state of traffic
based on historical situations. This problem has received ever-increasing attention in …

In this paper, we study semi-supervised graph classification, which aims at accurately
predicting the categories of graphs in scenarios with limited labeled graphs and abundant …

Abstract Semi-Supervised Graph Learning (SSGL) aims to predict massive unknown labels
based on a subset of known labels within a graph. Recently, graph neural network, one of …

Graph convolutional networks have significant advantages in dealing with graph-structured
data, but most existing methods usually potentially assume that nodes belonging to the …

Node classification, seeking to predict the categories of unlabeled nodes, is a crucial task in
graph learning. One of the most popular methods for node classification is currently Graph …

Heterogeneous graphs, with diverse node and edge types, are prevalent in real-world
scenarios. Graph Neural Networks have gained significant attention for processing such …

Abstract Graph Neural Networks (GNNs) have exhibited significant success in various
applications, but they face challenges when labeled nodes are limited. A novel self …

Graph neural networks (GNNs) have achieved great success in many scenarios with graph-
structured data. However, in many real applications, three issues arise when applying …