ASIND: Alternating Sparse Identification for Predicting Network Dynamics Without Knowledge

2026-05-20

AI Summary

Modeling complex network dynamics in an efficient and interpretable manner remains highly challenging when prior knowledge of the intrinsic nodal dynamics, interaction functions, and underlying network topology is unavailable. This work proposes Alternating Sparse Identification of Network Dynamics (ASIND), a method that leverages an alternating optimization strategy combined with sparse regression to simultaneously reconstruct these three core components without any prior information. ASIND achieves, for the first time, joint sparse identification of all three elements of network dynamics, balancing model interpretability with strong multi-step predictive capability. The approach also uncovers a phenomenon of weak identifiability in interaction networks. Experimental results demonstrate that ASIND attains state-of-the-art performance in both dynamical system identification accuracy and 100-step-ahead prediction.

Abstract

Identifying network dynamics is a critical yet challenging task to to understand the mechanism of real-world social systems. There are two types of algorithms, and one requires the knowledge of self-dynamics function, interactive function, and interactive network to sparsely identify the network dynamics. Another one does not require any knowledge, but use simple functions to universally approximate complex functions. However, this type of algorithms lack interpretability, and the functional space is too extensive to search efficiently. Thus, to address this issue, this work proposes an Alternating Sparse Identification of Network Dynamics (ASIND) algorithm to sparsely identify the self-dynamics function, interactive function and interactive network alternatively. Extensive experiments are conducted to show the state-of-the-art identification and 100-steps prediction performance compared to the baseline. The experimental results also show the weak identifiability of interactive network, that means different networks can generate highly similar trajectories of network dynamics. The code is available at https://github.com/KMY-SEU/ASIND.

Problem

• network dynamics
• sparse identification
• self-dynamics function
• interactive function
• interpretable modeling

Innovation

• Alternating Sparse Identification
• Network Dynamics Prediction
• Self-dynamics Function
• Interactive Function
• Interpretability