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.

翻译：图神经网络（GNNs）在处理图数据方面已展现出显著成效，但其可扩展性仍面临重大挑战。为解决这一问题，研究者们开发了多种图粗化方法。然而，现有的大多数粗化方法依赖于训练过程，导致效率较低，且均需预先设定粗化率，缺乏自适应机制。本文采用粒度球计算来有效压缩图数据。我们基于纯度阈值迭代地将图分割为粒度球，并将这些粒度球作为超顶点，从而构建出一个粗化图网络。该粒度化过程显著减小了原始图的规模，从而极大提升了GNNs的训练效率与可扩展性。此外，我们的算法能够自适应地进行分割，无需预先设定粗化率。实验结果表明，本方法所达到的准确率可与在原始图上训练相媲美。噪声注入实验进一步表明，本方法展现出鲁棒的性能。此外，我们的方法可在不损害测试准确率的前提下将图规模缩减高达20倍，显著增强了GNNs的可扩展性。