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 received significant attention for demonstrating their
capability to handle graph data. However, they are difficult to be deployed in resource …

Graph neural networks, a powerful deep learning tool to model graph-structured data, have
demonstrated remarkable performance on numerous graph learning tasks. To address the …

Spatio-temporal predictive learning is a learning paradigm that enables models to learn
spatial and temporal patterns by predicting future frames from given past frames in an …

Federated Graph Learning is a privacy-preserving collaborative approach for training a
shared model on graph-structured data in the distributed environment. However, in real …

In many real-world applications, graph-structured data used for training and testing have
differences in distribution, such as in high energy physics (HEP) where simulation data used …

Graph machine learning has been extensively studied in both academia and industry.
Although booming with a vast number of emerging methods and techniques, most of the …

To bridge the gaps between topology-aware Graph Neural Networks (GNNs) and inference-
efficient Multi-Layer Perceptron (MLPs), GLNN proposes to distill knowledge from a well …

Recent years have witnessed the great success of Graph Neural Networks (GNNs) in
handling graph-related tasks. However, MLPs remain the primary workhorse for practical …

Protein inverse folding has attracted increasing attention in recent years. However, we
observe that current methods are usually limited to the CATH dataset and the recovery …