IEEE

Distributed Training of Large Graph Neural Networks With Variable Communication Rates

Abstract: Training Graph Neural Networks (GNNs) on large graphs presents unique challenges due to the large memory and computing requirements. Distributed GNN training, where the graph is partitioned ...
GitHub

HCGNN: Two-Level Historical Caching for Large-Scale Out-of-GPU-Core GNN Training

Accelerating Large-Scale Out-of-GPU-Core GNN Training with Two-Level Historical Caching (Accepted by APPT 2025) HCGNN is a high-performance system for training Graph Neural Networks (GNNs) on ...
GitHub

GraphSAINT: Graph Sampling Based Inductive Learning Method

In fact, graph sampling can also be understood as data augmentation or training regularization (e.g., we may see the edge sampling as a minibatch version of DropEdge). Efficiency: While "neighbor ...
IEEE

GI-Graph: A Generative Invariant Graph Learning Scheme Towards Out-of-Distribution Generalization

Abstract: When distribution shifts occur between testing and training graph data, out-of-distribution (OOD) samples undermine the performance of graph neural networks (GNNs). To improve adaptive OOD ...