The rapid growth of graph data poses significant challenges in storage, transmission, and
particularly the training of graph neural networks (GNNs). To address these challenges …

Many real-world datasets can be naturally represented as graphs, spanning a wide range of
domains. However, the increasing complexity and size of graph datasets present significant …

Despite Graph Neural Networks (GNNs) have achieved prominent success in many graph-
based learning problem, such as credit risk assessment in financial networks and fake news …

Analytics on large-scale graphs have posed significant challenges to computational
efficiency and resource requirements. Recently, Graph condensation (GC) has emerged as …

Recently, graph condensation has emerged as a prevalent technique to improve the training
efficiency for graph neural networks (GNNs). It condenses a large graph into a small one …

Dataset Condensation has emerged as a technique for compressing large datasets into
smaller synthetic counterparts, facilitating downstream training tasks. In this paper, we study …

In the era of rapidly expanding graph-based applications, efficiently managing large-scale
graphs has become a critical challenge. This paper introduces an innovative graph …

Large-scale graphs are valuable for graph representation learning, yet the abundant data in
these graphs hinders the efficiency of the training process. Graph condensation (GC) …

Deep neural networks are ubiquitously adopted in many applications, such as computer
vision, natural language processing, and graph analytics. However, well-trained neural …

The increasing prevalence of large-scale graphs poses a significant challenge for graph
neural network training, attributed to their substantial computational requirements. In …