TY  - JOUR
VL  - 56
Y1  - 2016///
JF  - Engineering Applications of Artificial Intelligence
PB  - Elsevier
A1  - Morisi, Rita
A1  - Gnecco, Giorgio
A1  - Bemporad, Alberto
SP  - 157 
N1  - SCOPUS ID: 2-s2.0-84987968759
ID  - eprints3547
EP  -  174
KW  - Consensus problem; Approximation; Hierarchical consensus; Clustering; Spectral graph theory
AV  - none
TI  - A hierarchical consensus method for the approximation of the consensus state, based on clustering and spectral graph theory
UR  - http://www.sciencedirect.com/science/article/pii/S0952197616301592
SN  - 0952-1976
N2  - A hierarchical method for the approximate computation of the consensus state of a network of agents is investigated. The method is motivated theoretically by spectral graph theory arguments. In a first phase, the graph is divided into a number of subgraphs with good spectral properties, i.e., a fast convergence toward the local consensus state of each subgraph. To find the subgraphs, suitable clustering methods are used. Then, an auxiliary graph is considered, to determine the final approximation of the consensus state in the original network. A theoretical investigation is performed of cases for which the hierarchical consensus method has a better performance guarantee than the non-hierarchical one (i.e., it requires a smaller number of iterations to guarantee a desired accuracy in the approximation of the consensus state of the original network). Moreover, numerical results demonstrate the effectiveness of the hierarchical consensus method for several case studies modeling real-world networks.
ER  -