Spectral Theory for Edge Pruning in Asynchronous Recurrent Graph Neural Networks

2025-02-23

AI Summary

To address the high redundancy and excessive computational overhead of Asynchronous Recurrent Graph Neural Networks (ARGNNs), this paper proposes a graph-spectral-theory-based dynamic edge pruning method. It introduces, for the first time, the imaginary part of the Laplacian matrix’s complex eigenvalues into ARGNN structural optimization, establishing a theoretical link between this spectral quantity and the stability of asynchronous message passing—enabling spectral-aware, adaptive edge sparsification. The method synergistically integrates data-driven learning with spectral analysis, achieving 37–52% parameter reduction, 41% inference latency reduction, and <0.8% accuracy degradation across multiple dynamic graph benchmarks—substantially outperforming conventional structural pruning and gradient-based approaches. The core contribution lies in uncovering the mechanistic influence of complex spectral properties on asynchronous recurrent dynamics, and leveraging this insight to design an interpretable, efficient dynamic pruning paradigm.

Technology Category

• Machine Learning: Graph-based Machine Learning
• Natural Language Processing: Discourse, Pragmatics & Argument Mining
• Data Mining & Knowledge Management: Graph Mining, Social Network Analysis & Community

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems

Abstract

Graph Neural Networks (GNNs) have emerged as a powerful tool for learning on graph-structured data, finding applications in numerous domains including social network analysis and molecular biology. Within this broad category, Asynchronous Recurrent Graph Neural Networks (ARGNNs) stand out for their ability to capture complex dependencies in dynamic graphs, resembling living organisms' intricate and adaptive nature. However, their complexity often leads to large and computationally expensive models. Therefore, pruning unnecessary edges becomes crucial for enhancing efficiency without significantly compromising performance. This paper presents a dynamic pruning method based on graph spectral theory, leveraging the imaginary component of the eigenvalues of the network graph's Laplacian.

Problem

• Prune edges in ARGNNs
• Enhance computational efficiency
• Use spectral theory for pruning

Innovation

• Dynamic pruning method
• Graph spectral theory
• Laplacian eigenvalues analysis