[2402.08412] Interacting Particle Systems on Networks: joint inference of the network and the interaction kernel

Statistics > Machine Learning arXiv:2402.08412 (stat) [Submitted on 13 Feb 2024 (v1), last revised 23 Mar 2026 (this version, v2)] Title:Interacting Particle Systems on Networks: joint inference of the network and the interaction kernel Authors:Quanjun Lang, Xiong Wang, Fei Lu, Mauro Maggioni View a PDF of the paper titled Interacting Particle Systems on Networks: joint inference of the network and the interaction kernel, by Quanjun Lang and 2 other authors View PDF HTML (experimental) 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 numerica...