Leading graph ordinary differential equation (ODE) models have offered generalized
strategies to model interacting multi-agent dynamical systems in a data-driven approach …

Abstract Graph Contrastive Learning (GCL) has emerged to learn generalizable
representations from contrastive views. However, it is still in its infancy with two concerns: 1) …

We present a unified and flexible framework to address the generalized problem of 3D
motion synthesis that covers the tasks of motion prediction, completion, interpolation, and …

Abstract Temporal Graph Networks (TGNs) are powerful on modeling temporal graph data
based on their increased complexity. Higher complexity carries with it a higher risk of …

Graph neural networks (GNNs) have achieved state-of-the-art results on graph classification
tasks. They have been primarily studied in cases of supervised end-to-end training, which …

Graph Neural Networks (GNNs) are playing increasingly important roles in critical decision-
making scenarios due to their exceptional performance and end-to-end design. However …

Abstract Graph Neural Networks (GNNs) have achieved great success in representing data
with dependencies by recursively propagating and aggregating messages along the edges …

Node classification is a fundamental graph-based task that aims to predict the classes of
unlabeled nodes, for which Graph Neural Networks (GNNs) are the state-of-the-art methods …

The history of artificial intelligence (AI) has witnessed the significant impact of high-quality
data on various deep learning models, such as ImageNet for AlexNet and ResNet. Recently …

Given a graph dataset, how can we augment it for accurate graph classification? Graph
augmentation is an essential strategy to improve the performance of graph-based tasks, and …