用零强迫机制生成保留学习特性的图骨架，提升图学习效率。
Learning Backbones: Sparsifying Graphs through Zero Forcing for Effective Graph-Based Learning
- 基于零强迫动态生成保留关键性质的图树结构
- 在8个数据集上优于现有方法，显著降低计算复杂度
- 适合需要高效图学习的科研与工业场景
本文提出一种新型图稀疏化框架，通过零强迫（Zero Forcing, ZF）现象生成保留原始图关键学习属性的稀疏图，称为“学习骨架”。该方法利用图上的动态过程构建一棵树结构，以保持重要的动力学特性，并将其与学习属性关联，从而构造高效的图学习骨架。我们在八个数据集和六种基准模型上评估了基于ZF的骨架在图分类任务中的性能，结果表明该方法优于现有技术。此外，还探索了使用节点距离度量对框架进行扩展，进一步提升了实用性。
原文摘要 · Abstract (English)
This paper introduces a novel framework for graph sparsification that preserves the essential learning attributes of original graphs, improving computational efficiency and reducing complexity in learning algorithms. We refer to these sparse graphs as "learning backbones". Our approach leverages the zero-forcing (ZF) phenomenon, a dynamic process on graphs with applications in network control. The key idea is to generate a tree from the original graph that retains critical dynamical properties. By correlating these properties with learning attributes, we construct effective learning backbones. We evaluate the performance of our ZF-based backbones in graph classification tasks across eight datasets and six baseline models. The results demonstrate that our method outperforms existing techniques. Additionally, we explore extensions using node distance metrics to further enhance the framework's utility.
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