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 …

In the field of traffic forecasting, methods based on Graph Convolutional Network (GCN) are
emerging. But existing methods still have limitations due to insufficient sharing patterns …

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

Providing potential next point-of-interest (POI) suggestions for users has become a
prominent task in location-based social networks, which receives more and more attention …

Sequential recommendation systems aim to predict the future behaviors of users based on
their historical interactions. Despite the success of neural architectures like Transformer and …

In recent years, deep learning on graphs has achieved remarkable success in various
domains. However, the reliance on annotated graph data remains a significant bottleneck …

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 …

Graph classification is a crucial task in many real-world multimedia applications, where
graphs can represent various multimedia data types such as images, videos, and social …

Sequential recommender systems often struggle with accurate personalized
recommendations due to data sparsity issues. Existing works use variational autoencoders …

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 …