Graph Neural Networks (GNNs) have exploded onto the machine learning scene in recent
years owing to their capability to model and learn from graph-structured data. Such an ability …

Foundation models have emerged as critical components in a variety of artificial intelligence
applications, and showcase significant success in natural language processing and several …

Abstract Spatio-Temporal Graph (STG) forecasting is a fundamental task in many real-world
applications. Spatio-Temporal Graph Neural Networks have emerged as the most popular …

Causal inference has numerous real-world applications in many domains, such as health
care, marketing, political science, and online advertising. Treatment effect estimation, a …

Graph convolutional network (GCN) outputs powerful representation by considering the
structure information of the data to conduct representation learning, but its robustness is …

Most state-of-the-art Graph Neural Networks focus on node features in the learning process
but ignore edge features. However, edge features also contain essential information in real …

Graph neural networks have recently achieved remarkable success in representing graph-
structured data, with rapid progress in both the node embedding and graph pooling …

Node classification is an important problem on graphs. While recent advances in graph
neural networks achieve promising performance, they require abundant labeled nodes for …

Graph, as an important data representation, is ubiquitous in many real world applications
ranging from social network analysis to biology. How to correctly and effectively learn and …

Graph neural networks (GNNs) have significant advantages in dealing with non-Euclidean
data and have been widely used in various fields. However, most of the existing GNN …