Resilient Monitoring of Social Dynamical Systems through Collaborative Multi-Agent Networks under Latency
Abstract
Social dynamical networks significantly influence contemporary digital landscapes, affecting realms from social activism to public policy formulation. This paper investigates the use of multi-agent systems (MAS) to monitor and analyze these networks. Firstly, we propose a single-time-scale distributed inference model designed to effectively manage challenges such as latency and agent failure. Secondly, we provide sufficient conditions that ensure the stability of the proposed scheme. Notably, the observer gain design remains effective regardless of time delays. Thirdly, we develop a computationally efficient recovery mechanism for agent failures that relies on employing graph-theoretic approaches to restore network observability by replacing failed agents (due to sensing failure or unbounded delays, i.e., packet drops) by implementing computationally efficient graph-theoretic methods to assign observationally equivalent agent counterparts. Lastly, we illustrate the proposed scheme through a pedagogical example and real-world network applications.
Related
- Resilient Decentralized Preconditioned Stochastic Proximal Optimization with Gradient Sketching over Time-Varying Networks
- Majority Dynamics on Resampled Sparse Erdős--Rényi Graphs: Gaussian Winner Selection in Collaborative Agent Networks
- Recent Advances in Resilient Multi-Energy Systems Against Climate Change
- Dynamic Trust-Driven Resilient Finite-/Fixed-Time Formation Control for Multi-UAV Systems with Input Saturation Against Sybil Attacks
- Spectral Differential Properties of the Distributional FKF Transform with Applications to Resilient Supply-Chain Systems
BibTeX
@misc{doostmohammadian2026resilient,
title = {{Resilient Monitoring of Social Dynamical Systems through Collaborative Multi-Agent Networks under Latency}},
author = {Mohammadreza Doostmohammadian and Sergio Pequito},
howpublished = {arXiv},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2609.27902},
eprint = {2609.27902},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2609.27902},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Resilient Monitoring of Social Dynamical Systems through Collaborative Multi-Agent Networks under Latency
AU - Mohammadreza Doostmohammadian
AU - Sergio Pequito
PY - 2026
DA - 2026/09/01
PB - arXiv
DO - 10.48550/arxiv.2609.27902
UR - https://arxiv.org/abs/2609.27902
AB - Social dynamical networks significantly influence contemporary digital landscapes, affecting realms from social activism to public policy formulation. This paper investigates the use of multi-agent systems (MAS) to monitor and analyze these networks. Firstly, we propose a single-time-scale distributed inference model designed to effectively manage challenges such as latency and agent failure. Secondly, we provide sufficient conditions that ensure the stability of the proposed scheme. Notably, the observer gain design remains effective regardless of time delays. Thirdly, we develop a computationally efficient recovery mechanism for agent failures that relies on employing graph-theoretic approaches to restore network observability by replacing failed agents (due to sensing failure or unbounded delays, i.e., packet drops) by implementing computationally efficient graph-theoretic methods to assign observationally equivalent agent counterparts. Lastly, we illustrate the proposed scheme through a pedagogical example and real-world network applications.
ER -