Graph Coarsening via Supervised Granular-Ball for Scalable Graph Neural Network Training
This addresses scalability issues for researchers and practitioners using GNNs on large graphs, though it is incremental as it builds on existing coarsening methods.
The paper tackles the scalability challenge in Graph Neural Networks (GNNs) by proposing a graph coarsening method using granular-ball computing, which reduces graph size by up to 20 times without compromising test accuracy, thereby enhancing training efficiency and scalability.
Graph Neural Networks (GNNs) have demonstrated significant achievements in processing graph data, yet scalability remains a substantial challenge. To address this, numerous graph coarsening methods have been developed. However, most existing coarsening methods are training-dependent, leading to lower efficiency, and they all require a predefined coarsening rate, lacking an adaptive approach. In this paper, we employ granular-ball computing to effectively compress graph data. We construct a coarsened graph network by iteratively splitting the graph into granular-balls based on a purity threshold and using these granular-balls as super vertices. This granulation process significantly reduces the size of the original graph, thereby greatly enhancing the training efficiency and scalability of GNNs. Additionally, our algorithm can adaptively perform splitting without requiring a predefined coarsening rate. Experimental results demonstrate that our method achieves accuracy comparable to training on the original graph. Noise injection experiments further indicate that our method exhibits robust performance. Moreover, our approach can reduce the graph size by up to 20 times without compromising test accuracy, substantially enhancing the scalability of GNNs.