Convergence Rate Analysis of Average Consensus Dynamics in Sparse Activity-driven Network

Abstract

This paper investigates the convergence performance of activity-driven networks with a focus on the average consensus problem in multi-agent systems. In order to accurately represent real networks, temporal network models are crucial. To account for the sparsity observed in real networks, we propose a novel model of temporal networks called the sparse activity-driven network model. For the proposed network model, we then present an upper bound on the convergence rate of the average consensus protocol. We further extend our study to simplicial activity-driven networks. Our research highlights the challenges of using conventional methods to analyze the convergence performance in activity-driven networks, and provides valuable insights into how to improve the performance through the sparse and simplicial approaches.