Impact of clustering on the observability and controllability of complex networks

2026-01-01

Cluster Computing

AI Summary

This study investigates the impact of clustering structure on the observability and controllability of scale-free complex networks, with the aim of optimizing the placement of driver and observer nodes. Drawing upon structural systems theory and employing Monte Carlo simulations alongside multiple case studies, the work quantifies the relationship between the clustering coefficient and the required resources for control and observation. The findings reveal that networks with higher clustering coefficients significantly reduce the number of driver and observer nodes needed, owing to more efficient internal information propagation. These results provide a theoretical foundation and practical guidance for deploying sensors and actuators in resource-constrained applications such as social networks and intelligent transportation systems.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization
• Search and Optimization: Mixed Discrete/Continuous Search

Application Category

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

Abstract

The increasing complexity and interconnectedness of systems across various fields have led to a growing interest in studying complex networks, particularly Scale-Free (SF) networks, which best model real-world systems. This paper investigates the influence of clustering on the observability and controllability of complex SF networks, framing these characteristics in the context of structured systems theory. In this paper, we show that densely clustered networks require fewer driver and observer nodes due to better information propagation within clusters. This relationship is of interest for optimizing network design in applications such as social networks and intelligent transportation systems. We first quantify the network observability/controllability requirements, and then, through Monte-Carlo simulations and different case studies, we show how clustering affects these metrics. Our findings offer practical insights into reducing control and observer nodes for sensor/actuator placement, particularly in resource-constrained setups. This work contributes to the understanding of network observability/controllability and presents techniques for improving these features through alterations in network structure and clustering.

Problem

• clustering
• observability
• controllability
• complex networks
• scale-free networks

Innovation

• clustering
• observability
• controllability
• scale-free networks
• structured systems theory