Paper Info
Title
Katz Centrality Of Markovian Temporal Networks: Analysis And Optimization
Abstract
Identifying important nodes in complex networks is a fundamental problem in network analysis. Although a plethora of measures has been proposed to identify important nodes in static (i.e., time-invariant) networks, there is a lack of tools in the context of temporal networks (i.e., networks whose connectivity dynamically changes over time). The aim of this paper is to propose a system-theoretic approach for identifying important nodes in temporal networks. In this direction, we first propose a generalization of the popular Katz centrality measure to the family of Markovian temporal networks using tools from the theory of Markov jump linear systems. We then show that Katz centrality in Markovian temporal networks can be efficiently computed using linear programming. Finally, we propose a convex program for optimizing the Katz centrality of a given node by tuning the weights of the temporal network in a cost-efficient manner. Numerical simulations illustrate the effectiveness of the obtained results.
Year
DOI
Venue
2017
10.23919/ACC.2017.7963730
2017 AMERICAN CONTROL CONFERENCE (ACC)
DocType
Volume
ISSN
Conference
abs/1609.05962
0743-1619
Citations 
PageRank 
References 
2
0.37
11
Authors
2
Name
Order
Citations
PageRank
Masaki Ogura14413.38
Victor M. Preciado220529.44