Out-of-distribution (OOD) generalization has gained increasing attentions for learning on
graphs, as graph neural networks (GNNs) often exhibit performance degradation with …

Graph neural networks have become the primary graph representation learning paradigm,
in which nodes update their embeddings by aggregating messages from their neighbors …

Heterogeneous graphs with multiple types of nodes and link relationships are ubiquitous in
many real-world applications. Heterogeneous graph neural networks (HGNNs) as an …

Graphs can model complicated interactions between entities, which naturally emerge in
many important applications. These applications can often be cast into standard graph …

General graph neural networks (GNNs) implement convolution operations on graphs based
on polynomial spectral filters. Existing filters with high-order polynomial approximations can …

Graph neural networks (GNNs) with adaptive propagation combinations represent a
specialized deep learning paradigm, engineered to capture complex nodal interconnections …

Spatiotemporal forecasting in various domains, like traffic prediction and weather
forecasting, is a challenging endeavor, primarily due to the difficulties in modeling …

Graph neural networks (GNNs) with feature propagation have demonstrated their power in
handling unstructured data. However, feature propagation is also a smooth process that …

Quantum machine learning is a fast-emerging field that aims to tackle machine learning
using quantum algorithms and quantum computing. Due to the lack of physical qubits and …

It has been discovered that graph convolutional networks (GCNs) encounter a remarkable
drop in performance when multiple layers are piled up. The main factor that accounts for why …