Iterative structural coarse-graining for contagion dynamics in complex networks

TL;DR

The paper introduces ISCG, a scalable framework for reducing complex network size while accurately preserving contagion dynamics, enabling efficient analysis of large-scale epidemic and information spread.

Contribution

It presents a novel iterative coarse-graining method with theoretical guarantees for maintaining key contagion properties during network reduction.

Findings

ISCG achieves significant network simplification with high fidelity.

Outperforms traditional methods in identifying influential nodes and optimizing interventions.

Enables scalable analysis of contagion processes in large networks.

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.