On the Relation between Centrality Measures and Consensus Algorithms

TL;DR

This paper develops new centrality measures based on degree distribution to optimize and analyze hierarchical consensus algorithms for large-scale decision-making, demonstrating improved robustness and efficiency in network control.

Contribution

It introduces a novel class of degree-based centrality measures and applies them to design hierarchical consensus structures, enhancing robustness and performance.

Findings

The new centrality measure outperforms standard measures in convergence.

Hierarchical structures improve robustness in large-scale networks.

Application to Gas Transmission Network validates the method's effectiveness.

Abstract

This paper introduces some tools from graph theory and distributed consensus algorithms to construct an optimal, yet robust, hierarchical information sharing structure for large-scale decision making and control problems. The proposed method is motivated by the robustness and optimality of leaf-venation patterns. We introduce a new class of centrality measures which are built based on the degree distribution of nodes within network graph. Furthermore, the proposed measure is used to select the appropriate weight of the corresponding consensus algorithm. To this end, an implicit hierarchical structure is derived that control the flow of information in different situations. In addition, the performance analysis of the proposed measure with respect to other standard measures is performed to investigate the convergence and asymptotic behavior of the measure. Gas Transmission Network is served as our test-bed to demonstrate the applicability and the efficiently of the method.