LOCAL NODE COMPENSATION FOR ENHANCED STABILITY IN DISTRIBUTED SIGNED NETWORKS (International Journal of Modern Computer Science and IT Innovations, 2025)
Multi-agent systems exhibiting both cooperative and antagonistic interactions, often modeled as signed networks, present unique challenges for achieving system stability and desired collective behaviors. Traditional consensus algorithms, primarily designed for purely cooperative networks, often fail in the presence of negative links, leading to phenomena like polarization or divergence. This article introduces a novel distributed stabilization strategy for signed networks based on local node compensation, effectively adding self-loops to individual agents. By leveraging this compensatory mechanism at each node, agents can autonomously adjust their dynamics to counteract the destabilizing effects of antagonistic connections, thereby promoting system stability. We detail the theoretical framework for incorporating self-loop compensation into standard agent dynamics and analyze its impact on the network's spectral properties. Hypothetical results demonstrate that this localized intervention significantly enhances the stability margin and convergence characteristics, offering a scalable and implementable solution for maintaining coherent behavior in complex signed multi-agent environments.