Graph Condensation for Graph Neural Networks
This addresses efficiency concerns for researchers and practitioners working with large-scale graphs, though it is an incremental improvement on existing graph condensation methods.
The paper tackles the problem of reducing storage and training time for graph neural networks (GNNs) by condensing large graphs into small synthetic ones, achieving test accuracy approximations of 95.3% on Reddit, 99.8% on Flickr, and 99.0% on Citeseer while reducing graph size by over 99.9%.
Given the prevalence of large-scale graphs in real-world applications, the storage and time for training neural models have raised increasing concerns. To alleviate the concerns, we propose and study the problem of graph condensation for graph neural networks (GNNs). Specifically, we aim to condense the large, original graph into a small, synthetic and highly-informative graph, such that GNNs trained on the small graph and large graph have comparable performance. We approach the condensation problem by imitating the GNN training trajectory on the original graph through the optimization of a gradient matching loss and design a strategy to condense node futures and structural information simultaneously. Extensive experiments have demonstrated the effectiveness of the proposed framework in condensing different graph datasets into informative smaller graphs. In particular, we are able to approximate the original test accuracy by 95.3% on Reddit, 99.8% on Flickr and 99.0% on Citeseer, while reducing their graph size by more than 99.9%, and the condensed graphs can be used to train various GNN architectures.Code is released at https://github.com/ChandlerBang/GCond.