Work

Predicting steady-state behavior in complex networks with graph neural networks

Year: 2025

Type: article

Abstract: In complex systems, information propagation can be defined as diffused or delocalized, weakly localized, and strongly localized. This study investigates the application of graph neural network models to learn the behavior of a linear dynamical system on networks. A graph convolution and attention-based neural network framework has been developed to identify the steady-state behavior of the linear dynamical system. We reveal that our trained model distinguishes the different states with high accuracy. Furthermore, we have evaluated model performance with real-world data. In addition, to understand the explainability of our model, we provide an analytical derivation for the forward and backward propagation of our framework.

Source: Physical review. E

DOI: 10.1103/rkp2-3d1r

Authors: Priodyuti Pradhan, Amit Reza

Institutions: Space Research Institute, National Institute for Subatomic Physics

Language: English

Topic: Complex Network Analysis Techniques

Subfield: Statistical and Nonlinear Physics

Field: Physics and Astronomy

Domain: Physical Sciences

Open Access status: green

Funders: Arthritis National Research Foundation, Indian Institute of Technology Gandhinagar, National Research Foundation, Nederlandse Organisatie voor Wetenschappelijk Onderzoek

Award: G1828922799