On the Relation between Centrality Measures and Consensus Algorithms
This work addresses the need for robust and optimal information sharing in large-scale control systems, but the contribution is incremental as it combines existing graph theory and consensus concepts with a new centrality measure.
The authors propose a new class of centrality measures based on degree distribution to construct optimal hierarchical information sharing structures for large-scale decision making, inspired by leaf-venation patterns. They demonstrate the method's applicability on a Gas Transmission Network, showing improved convergence and robustness compared to standard measures.
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.