Graph condensation, which reduces the size of a large-scale graph by synthesizing a small-
scale condensed graph as its substitution, has immediate benefits for various graph learning …

Abstract Graph Convolutional Networks (GCNs) are known to suffer from performance
degradation as the number of layers increases, which is usually attributed to over …

GCN is a widely-used representation learning method for capturing hidden features in graph
data. However, traditional GCNs suffer from the over-smoothing problem, hindering their …

Graph Neural Networks (GNNs) have become pivotal tools for a range of graph-based
learning tasks. Notably, most current GNN architectures operate under the assumption of …

Graph Neural Networks (GNNs) have succeeded in various computer science applications,
yet deep GNNs underperform their shallow counterparts despite deep learning's success in …

Active learning theories and methods have been extensively studied in classical statistical
learning settings. However, deep active learning, ie, active learning with deep learning …

Graph Neural Networks (GNNs) have emerged as the leading deep learning models for
graph-based representation learning. However, the training and inference of GNNs on large …

The emergence of larger and deeper graph neural networks (GNNs) makes their training
and inference increasingly expensive. Existing GNN pruning methods simultaneously prune …

Abstract Graph Neural Networks (GNNs) and Graph Kernels (GKs) are two fundamental
tools used to analyze graph-structured data. Efforts have been recently made in develo** …

Graph neural networks (GNNs) have pioneered advancements in graph representation
learning, exhibiting superior feature learning and performance over multilayer perceptrons …