Interacting Particle Systems on Networks: joint inference of the network and the interaction kernel

Abstract

Modeling multi-agent systems on networks is a fundamental challenge in a wide variety of disciplines. Given data consisting of multiple trajectories, we jointly infer the (weighted) network and the interaction kernel, which determine, respectively, which agents are interacting and the rules of such interactions. Our estimator is based on a non-convex optimization problem, and we investigate two approaches to solve it: one based on an alternating least squares (ALS) algorithm, and another based on a new algorithm named operator regression with alternating least squares (ORALS). Both algorithms are scalable to large ensembles of data trajectories. We establish coercivity conditions guaranteeing identifiability and well-posedness. The ALS algorithm appears statistically efficient and robust even in the small data regime, but lacks performance and convergence guarantees. The ORALS estimator is consistent and asymptotically normal under a coercivity condition. We conduct several numerical experiments ranging from Kuramoto particle systems on networks to opinion dynamics in leader-follower models.

arXiv comment53 pages, 17 figures

References in corpus (9)

• A Comprehensive Survey on Graph Neural Networks
• Guaranteed Minimum-Rank Solutions of Linear Matrix Equations via Nuclear Norm Minimization
• Hierarchical group dynamics in pigeon flocks
• User-friendly tail bounds for sums of random matrices
• Estimation of high-dimensional low-rank matrices
• The synchronized dynamics of time-varying networks
• Model-free inference of direct network interactions from nonlinear collective dynamics
• Nonparametric inference of interaction laws in systems of agents from trajectory data
• Autonomous inference of complex network dynamics from incomplete and noisy data