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A Connectivity-Sensitive Approach to Consensus Dynamics

Authors Bernard Chazelle , Kritkorn Karntikoon

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Author Details

Bernard Chazelle
  • Department of Computer Science, Princeton University, NJ, USA
Kritkorn Karntikoon
  • Department of Computer Science, Princeton University, NJ, USA

Funding

This work was supported in part by NSF grant CCF-2006125.

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Bernard Chazelle and Kritkorn Karntikoon. A Connectivity-Sensitive Approach to Consensus Dynamics. In 2nd Symposium on Algorithmic Foundations of Dynamic Networks (SAND 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 257, pp. 10:1-10:17, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023) https://doi.org/10.4230/LIPIcs.SAND.2023.10

Abstract

The paper resolves a long-standing open question in network dynamics. Averaging-based consensus has long been known to exhibit an exponential gap in relaxation time between the connected and disconnected cases, but a satisfactory explanation has remained elusive. We provide one by deriving nearly tight bounds on the s-energy of disconnected systems. This in turn allows us to relate the convergence rate of consensus dynamics to the number of connected components. We apply our results to opinion formation in social networks and provide a theoretical validation of the concept of an Overton window as an attracting manifold of "viable" opinions.

Subject Classification

ACM Subject Classification
  • Mathematics of computing → Combinatoric problems
  • Theory of computation → Dynamic graph algorithms
Keywords
  • s-energy
  • dynamic networks
  • relaxation time
  • multiagent systems

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