A physics-informed neural network model for social media user growth

Abstract

<p>In this paper, a physics-informed neural network model is proposed to predict the growth of online social network users. The number of online social network users is modeled by a stochastic process and the associated Kolmogorov forward equation is derived. Then, a physics-informed neural network model is built based on the Kolmogorov forward equation and trained using real-world data. By combining mathematical modeling with machine learning, our approach provides a practical and interpretable methodology that harnesses the strengths of both physical laws and advancements in machine learning, while minimizing the opacity in machine learning models.</p>