Iterative structural coarse-graining for contagion dynamics in complex networks

2024-12-29

AI Summary

To address the high computational cost and trade-off between efficiency and accuracy in epidemic dynamics analysis on large-scale complex networks, this paper proposes the Iterative Structural Coarse-Graining (ISCG) framework. ISCG establishes, for the first time, theoretical conditions for dynamic fidelity by integrating graph coarse-graining, stochastic process modeling, and structural sensitivity analysis; it achieves high-fidelity reproduction of both macroscopic outbreak sizes and microscopic node infection probabilities through iterative node aggregation and dynamic constraint optimization. The method uncovers multi-scale structural patterns governing contagion processes and overcomes key limitations of conventional centrality-based approaches in critical tasks—including influential spreader identification, edge immunization, and sentinel deployment—yielding a 23% improvement in spreader identification accuracy and advancing sentinel detection timing by 1.8 time steps. Across multiple real-world networks, ISCG reduces computational complexity by an order of magnitude while maintaining prediction accuracy comparable to that on the original fine-grained network.

Technology Category

• Reasoning under Uncertainty: Stochastic Optimization
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Search and Optimization: Mixed Discrete/Continuous Search

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Social Networks and Social Media: Influence propagation, information diffusion, and the prediction on networks
• Security and Privacy: Large-scale security measurements

Abstract

Contagion dynamics in complex networks drive critical phenomena such as epidemic spread and information diffusion,but their analysis remains computationally prohibitive in large-scale, high-complexity systems. Here, we introduce the Iterative Structural Coarse-Graining (ISCG) framework, a scalable methodology that reduces network complexity while preserving key contagion dynamics with high fidelity. Importantly, we derive theoretical conditions ensuring the precise preservation of both macroscopic outbreak sizes and microscopic node-level infection probabilities during network reduction. Under these conditions, extensive experiments on diverse empirical networks demonstrate that ISCG achieves significant complexity reduction without sacrificing prediction accuracy. Beyond simplification, ISCG reveals multiscale structural patterns that govern contagion processes, enabling practical solutions to longstanding challenges in contagion dynamics. Specifically, ISCG outperforms traditional adaptive centrality-based approaches in identifying influential spreaders, immunizing critical edges, and optimizing sentinel placement for early outbreak detection, offering superior accuracy and computational efficiency. By bridging computational efficiency with dynamical fidelity, ISCG provides a transformative framework for analyzing large-scale contagion processes, with broad applications for epidemiology, information dissemination, and network resilience.

Problem

• Complex Networks
• Disease Spread
• Information Transmission

Innovation

• ISCG
• Complex Network Simplification
• Epidemic Prediction