Learning Backbones: Sparsifying Graphs through Zero Forcing for Effective Graph-Based Learning

2025-02-24

AI Summary

This work addresses the high computational redundancy and complexity in graph learning by proposing a graph sparsification framework grounded in Zero Forcing (ZF) dynamics, yielding a “learning backbone graph” that preserves essential learnability properties. It establishes, for the first time, a theoretical linkage between ZF processes and graph learnability, using dynamic controllability as a principled criterion for structural simplification. The method introduces a tree-structured backbone construction paradigm and incorporates node-distance-weighted pruning to enhance robustness. Extensive experiments across eight benchmark graph datasets and six representative baseline models demonstrate that the approach significantly improves inference efficiency while achieving, on average, higher classification accuracy than state-of-the-art sparsification methods—thereby validating its effectiveness and generalizability.

Technology Category

• Machine Learning: Graph-based Machine Learning
• Reasoning under Uncertainty: Graphical Models
• Search and Optimization: Learning to Search

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies

Abstract

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.

Problem

• Graph sparsification preserving learning attributes
• Improving computational efficiency in learning algorithms
• Using zero-forcing for effective graph-based learning

Innovation

• Zero-forcing sparsification technique
• Tree generation preserving dynamics
• Enhanced graph classification performance