Distributed Articulation Point Identification in Time-Varying Undirected Networks

2025-12-03

AI Summary

In time-varying undirected networks, critical nodes (articulation points) must be re-identified in real time to assess network robustness, yet existing centralized approaches incur prohibitive computational and communication overhead. Method: This paper proposes a fully distributed, incremental update algorithm grounded in the max-consensus protocol. It updates only the states of nodes affected by edge insertions or deletions via localized information exchange, eliminating global recomputation and reducing communication cost. Crucially, it embeds graph-theoretic articulation point detection criteria into the distributed consensus framework, enabling privacy-preserving, dynamic biconnectivity monitoring. Contribution/Results: We prove theoretical convergence to the exact set of articulation points. Experimental results on large-scale dynamic networks demonstrate substantial improvements over state-of-the-art methods in efficiency, scalability, and latency—achieving real-time adaptability without compromising accuracy.

Technology Category

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

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Security and Privacy: Large-scale security measurements

Abstract

Identifying articulation points (APs) is fundamental to assessing the robustness of time-varying networks. In such dynamic environments, topological changes including edge additions and deletions can instantly alter the set of APs, demanding rapid and efficient re-assessment. This paper proposes a fully distributed algorithm for identifying APs and monitoring biconnectivity. Our core contribution is an incremental update protocol. Unlike static methods that require global re-initialization which incurs high communication overhead, our algorithm propagates information from the site of the change, updating only the affected nodes' state values. This approach, which builds upon a maximum consensus protocol, not only ensures convergence to the correct AP set following topological changes but also preserves network privacy by preventing nodes from reconstructing the global topology. We provide rigorous proofs of correctness for this eventual convergence and demonstrate its applicability and efficiency through experiments.

Problem

• Identifies articulation points in dynamic networks
• Monitors biconnectivity with incremental updates
• Reduces communication overhead while preserving privacy

Innovation

• Incremental update protocol for dynamic networks
• Propagates changes locally to reduce overhead
• Uses maximum consensus for privacy and correctness