CONTROL CONTRIBUTION IDENTIFIES TOP DRIVER NODES IN COMPLEX NETWORKS

Yan Zhang (), Antonios Garas () and Frank Schweitzer
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Yan Zhang: Chair of Systems Design, ETH Zurich, Weinbergstrasse 58, 8092 Zurich, Switzerland
Antonios Garas: Chair of Systems Design, ETH Zurich, Weinbergstrasse 58, 8092 Zurich, Switzerland
Frank Schweitzer: Chair of Systems Design, ETH Zurich, Weinbergstrasse 58, 8092 Zurich, Switzerland

Advances in Complex Systems (ACS), 2019, vol. 22, issue 07n08, 1-15

Abstract: We propose a new measure to quantify the impact of a node i in controlling a directed network. This measure, called “control contribution” 𝒞i, combines the probability for node i to appear in a set of driver nodes and the probability for other nodes to be controlled by i. To calculate 𝒞i, we propose an optimization method based on random samples of minimum sets of drivers. Using real-world and synthetic networks, we find very broad distributions of Ci. Ranking nodes according to their Ci values allows us to identify the top driver nodes that can control most of the network. We show that this ranking is superior to rankings based on other control-based measures. We find that control contribution indeed contains new information that cannot be traced back to degree, control capacity or control range of a node.

Keywords: Complex network; structural controllability; driver nodes (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (4)

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DOI: 10.1142/S0219525919500140

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