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.

翻译：本文提出了一种新颖的图稀疏化框架，该框架能够保留原始图的关键学习属性，从而提升学习算法的计算效率并降低其复杂度。我们将这些稀疏图称为“学习骨架”。我们的方法利用了零迫现象——一种在图上的动态过程，在网络控制领域有广泛应用。其核心思想是从原始图中生成一棵树，该树保留了关键的动态特性。通过将这些特性与学习属性相关联，我们构建了有效的学习骨架。我们在八个数据集和六个基线模型上，针对图分类任务评估了基于零迫的骨架的性能。结果表明，我们的方法优于现有技术。此外，我们还探索了利用节点距离度量进行扩展，以进一步提升该框架的实用性。