Homophily principle,\ie {} nodes with the same labels or similar attributes are more likely to
be connected, has been commonly believed to be the main reason for the superiority of …

Graph neural networks (GNNs) have shown state-of-the-art performances in various
applications. However, GNNs often struggle to capture long-range dependencies in graphs …

Graphs are structured data that models complex relations between real-world entities.
Heterophilic graphs, where linked nodes are prone to be with different labels or dissimilar …

Sequential recommendation (SR) models based on Transformers have achieved
remarkable successes. The self-attention mechanism of Transformers for computer vision …

Graph Convolutional Networks (GCNs) are widely used for skeleton-based action
recognition and achieved remarkable performance. Due to the locality of graph convolution …

Graph neural networks (GNNs) have advanced the state of the art in various domains.
Despite their remarkable success, the uncertainty estimation of GNN predictions remains …

The integration of graph neural networks (GNNs) and neural ordinary and partial differential
equations has been extensively studied in recent years. GNN architectures powered by …

Abstract Graph Neural Networks (GNNs) have demonstrated remarkable success in
modeling complex relationships in graph-structured data. A recent innovation in this field is …

Transformers, renowned for their self-attention mechanism, have achieved state-of-the-art
performance across various tasks in natural language processing, computer vision, time …

Traffic forecasting is one of the most popular spatio-temporal tasks in the field of machine
learning. A prevalent approach in the field is to combine graph convolutional networks and …