Graph summarization as a preprocessing step is an effective and complementary technique for scalable graph neural network (GNN) training. In this work, we propose the Coarsening Via Convolution Matching (CONVMATCH) algorithm and a highly scalable variant, A-CONVMATCH, for creating summarized graphs that preserve the output of graph convolution. We evaluate CONVMATCH on six real-world link prediction and node classification graph datasets, and show it is efficient and preserves prediction performance while significantly reducing the graph size. Notably, CONVMATCH achieves up to 95% of the prediction performance of GNNs on node classification while trained on graphs summarized down to 1% the size of the original graph. Furthermore, on link prediction tasks, CONVMATCH consistently outperforms all baselines, achieving up to a 2x improvement.
@article{arxiv.2312.15520,
title = {Graph Coarsening via Convolution Matching for Scalable Graph Neural Network Training},
author = {Charles Dickens and Eddie Huang and Aishwarya Reganti and Jiong Zhu and Karthik Subbian and Danai Koutra},
journal= {arXiv preprint arXiv:2312.15520},
year = {2023}
}