Recent years have witnessed fast developments of graph neural networks (GNNs) that have
benefited myriads of graph analytic tasks and applications. In general, most GNNs depend …

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

Abstract Graph Structure Learning (GSL) has recently garnered considerable attention due
to its ability to optimize both the parameters of Graph Neural Networks (GNNs) and the …

Self-powered neurostimulation via biomaterials and bioelectronics innovation has emerged
as a compelling approach to explore, repair, and modulate neural systems. This review …

We investigate the replay buffer in rehearsal-based approaches for graph continual learning
(GCL) methods. Existing rehearsal-based GCL methods select the most representative …

Recent years have witnessed great success in handling graph-related tasks with Graph
Neural Networks (GNNs). Despite their great academic success, Multi-Layer Perceptrons …

In this survey, we dive into Tabular Data Learning (TDL) using Graph Neural Networks
(GNNs), a domain where deep learning-based approaches have increasingly shown …

Accurate prediction of protein-ligand binding structures, a task known as molecular docking
is crucial for drug design but remains challenging. While deep learning has shown promise …

In previous research, the prevailing assumption was that Graph Neural Networks (GNNs)
precisely depicted the interconnections among nodes within the graph's architecture …