Recent research has uncovered a promising way for understanding the mechanism behind task solving capabilities of (Graph) Neural Networks by establishing connections to continuous approximation algorithms. The clarification of neural network decision-making processes holds substantial implications for their design and contributes to the interpretability. In the presentation, I will share our findings, which shed light on the process of how Graph Neural Networks solve concrete problems such as boolean satisfiability.