Abstract
Networks pervade various domains, from molecular biology to social networks. However, empirical networks often exhibit stochastic behavior, challenging traditional graph theory-based approaches. Traditional approaches relying on graph theory face limitations in capturing the stochastic nature of empirical networks, motivating the need for alternative methodologies. Graph neural networks o…