In practical applications, multi-view data depicting objects from assorted perspectives can
facilitate the accuracy increase of learning algorithms. However, given multi-view data, there …

Graph Convolutional Networks (GCNs), as a prominent example of graph neural networks,
are receiving extensive attention for their powerful capability in learning node …

This paper delves into the problem of correlated time-series forecasting in practical
applications, an area of growing interest in a multitude of fields such as stock price …

Knowledge distillation (KD) is a learning paradigm for boosting resource-efficient graph
neural networks (GNNs) using more expressive yet cumbersome teacher models. Past work …

Graph neural networks (GNNs) have been successfully used to analyze non-Euclidean
network data. Recently, there emerge a number of works to investigate the robustness of …

Graph convolutional network (GCN) with the powerful capacity to explore graph-structural
data has gained noticeable success in recent years. Nonetheless, most of the existing GCN …

Plenty of models have been presented to handle the hypergraph node classification.
However, very few of these methods consider contrastive learning, which is popular due to …

Graph classification aims to predict the class labels of graphs and has a wide range of
applications in many real-world domains. However, most of existing graph neural networks …

Many real-world problems can be abstracted into graph classification problems. Recently,
graph convolutional networks have achieved great success in the task of node classification …

The goal of molecule optimization is to optimize molecular properties by modifying molecule
structures. Conditional generative models provide a promising way to transfer the input …