Advantages of Edge-centric Collective Dynamics in Machine Learning Tasks - Verri, Filipe Alves Neto; Urio, Paulo Roberto; Zhao, Liang (Journal of Applied Nonlinear Dynamics, 2026)
We study how effectively edge-centric dynamics solve semi-supervised learning tasks. The Edge Domination System is an algorithm to reveal patterns and obtain information of the underlying complex network. The algorithm consists of the simulation of a collective dynamical system based on particle competition for the dominance of edges. In this paper, we propose a vertex-centric version of this model and assess the differences between the edge-centric model. The edge-centric system offers better features in semi-supervised learning tasks, such as greater exploration behavior and faster convergence.