用粒球聚类自适应压缩图数据，提升GNN训练效率
Graph Coarsening via Supervised Granular-Ball for Scalable Graph Neural Network Training
- 基于纯度阈值迭代分割图成粒球，作为超顶点构建粗化图
- 可将图规模缩小20倍，测试准确率几乎不变
- 无需预设压缩率，适合大规模图神经网络训练
图神经网络在处理图数据方面已取得显著成果，但可扩展性仍是重大挑战。为应对这一问题，已提出多种图粗化方法。然而，大多数现有方法依赖训练过程，效率较低，且均需预先设定粗化率，缺乏自适应能力。本文采用粒球计算有效压缩图数据，通过基于纯度阈值迭代分割图并以粒球作为超顶点构建粗化图网络，大幅降低原始图规模，显著提升GNN的训练效率与可扩展性。此外，该算法能自适应执行分割，无需预设粗化率。实验表明，该方法在保持与原始图训练相当的准确率的同时，噪声注入实验进一步验证其鲁棒性。本方法可将图规模最多压缩20倍，且不牺牲测试精度，显著增强GNN的可扩展性。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。