Learning Backbones: Sparsifying Graphs through Zero Forcing for Effective Graph-Based Learning
This addresses computational complexity in graph learning, though it appears incremental as it builds on known zero-forcing methods.
The paper tackles the problem of graph sparsification for computational efficiency in graph-based learning by introducing 'learning backbones' using zero-forcing, and results show it outperforms existing techniques on eight datasets with six baseline models.
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.