The authors propose an implicit graph neural network (IGNN) to capture long-range dependencies in graphs. The proposed model is based on a fixed-point equilibrium equation. The authors first use the Perron-Frobenius theory to derive the well-posedness conditions of the model. Then, they introduce a trackable projection gradient descent method to effectively train the model. The authors evaluate their model on different data sets in different settings to demonstrate the ability of the model to capture long-range dependencies. The paper is suggested for publication.