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This content will become publicly available on April 11, 2026

Title: Learning Backbones: Sparsifying Graphs Through Zero Forcing for Effective Graph-Based Learning
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.  more » « less
Award ID(s):
2325416 2325417
PAR ID:
10626549
Author(s) / Creator(s):
; ; ; ;
Publisher / Repository:
Springer Nature Switzerland
Date Published:
ISBN:
978-3-031-82427-2
Page Range / eLocation ID:
85 to 100
Subject(s) / Keyword(s):
Sparsification Network Control Backbone Graph Neural Networks Graph Classification
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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