Preserving spreading dynamics and information flow in complex network reduction

TL;DR

This paper introduces an efficient network reduction method based on subgraph extraction that preserves epidemic spreading and information flow, significantly reducing network size while maintaining key dynamical properties.

Contribution

The paper presents a novel subgraph extraction framework with node removal and edge pruning algorithms that effectively preserve network dynamics during reduction.

Findings

Reduces network size by over 85% while preserving dynamics

Outperforms state-of-the-art reduction techniques in accuracy

Applicable to various network types including real-world social networks

Abstract

Effectively preserving both the structural and dynamical properties during the reduction of complex networks remains a significant research topic. Existing network reduction methods based on renormalization group or sampling often face challenges such as high computational complexity and the loss of critical dynamic attributes. This paper proposes an efficient network reduction framework based on subgraph extraction, which accurately preserves epidemic spreading dynamics and information flow through a coordinated optimization strategy of node removal and edge pruning. Specifically, a node removal algorithm driven by enhanced degree centrality is introduced to preferentially remove low-centrality nodes, thereby constructing a smaller-scale subnetwork. Subsequently, an edge pruning algorithm is designed to regulate the edge density of the subnetwork, ensuring that its average degree remains approximately consistent with that of the original network. Experimental results on Erd\"os-R\'enyi random graphs, Barab\'asi-Albert scale-free networks, and real-world social contact networks from various domains demonstrate that this proposed method can reduce the size of networks with heterogeneous structures by more than 85\%, while preserving their epidemic dynamics and information flow. More importantly, our method almost always achieves the highest accuracy compared to state-of-the-art techniques. These findings provide valuable insights for predicting the dynamical behavior of large-scale real-world networks, and also reveal that a large number of nodes and edges in real-world networks play redundant roles in information transmission.