Preserving spreading dynamics and information flow in complex network reduction

2025-06-23

AI Summary

Existing network reduction methods often suffer from loss of dynamic properties—such as epidemic spreading and information diffusion—and incur high computational overhead. To address these issues, this paper proposes a subgraph-extraction-based collaborative optimization framework for complex network reduction. The method innovatively integrates a degree-centrality-driven low-degree node prioritization strategy with an edge-density regulation mechanism, enabling synergistic optimization between node removal and edge pruning. This ensures that the reduced network preserves the original average degree and maintains high fidelity in key dynamical behaviors. The framework comprises four modular components: subgraph extraction, degree-centrality analysis, node removal, and edge pruning—ensuring scalability. Extensive experiments on synthetic random and scale-free networks, as well as diverse real-world social networks, demonstrate an average reduction ratio exceeding 85%, while dynamic metrics—including epidemic threshold and information diffusion curves—exhibit errors below 5%.

Technology Category

• Data Mining & Knowledge Management: Graph Mining, Social Network Analysis & Community
• Search and Optimization: Combinatorial Optimization
• Machine Learning: Graph-based Machine Learning

Application Category

• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Social Networks and Social Media: Influence propagation, information diffusion, and the prediction on networks
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems

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 degree centrality-driven node removal algorithm is adopted to preferentially remove low-degree 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 Erdös-Rényi random graphs, Barabási-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. These findings provide valuable insights for predicting the dynamical behavior of large-scale real-world networks.

Problem

• Preserving structural and dynamic properties in network reduction
• Addressing high computational complexity in existing methods
• Maintaining epidemic dynamics and information flow accurately

Innovation

• Subgraph extraction for network reduction
• Degree centrality-driven node removal
• Edge pruning to maintain average degree