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In today's era, users have increasingly high expectations regarding theperformance and efficiency of communication networks. Network operators aspireto achieve efficient network planning, operation, and optimization throughDigital Twin Networks (DTN). The effectiveness of DTN heavily relies on thenetwork model, with graph neural networks (GNN) playing a crucial role innetwork modeling. However, existing network modeling methods still lack acomprehensive understanding of communication networks. In this paper, wepropose DWNet (Deeper and Wider Networks), a heterogeneous graph neural networkmodeling method based on data-driven approaches that aims to address end-to-endlatency and jitter prediction in network models. This method stands out due totwo distinctive features: firstly, it introduces deeper levels of stateparticipation in the message passing process; secondly, it extensivelyintegrates relevant features during the feature fusion process. Throughexperimental validation and evaluation, our model achieves higher predictionaccuracy compared to previous research achievements, particularly when dealingwith unseen network topologies during model training. Our model not onlyprovides more accurate predictions but also demonstrates strongergeneralization capabilities across diverse topological structures.

Deeper and Wider Networks for Performance Metrics Prediction in Communication Networks