Graph Summarization for Efficient GNN Training

Our new paper studies how graph summarization can reduce training and sampling overhead for graph neural networks by removing redundant edges while preserving the accuracy, enabling faster and more memory-efficient learning on large graphs with minimal impact on model quality; the manuscript is currently under submission, and the arXiv version is coming soon.