  In today's era, users have increasingly high expectations regarding the
performance and efficiency of communication networks. Network operators aspire
to achieve efficient network planning, operation, and optimization through
Digital Twin Networks (DTN). The effectiveness of DTN heavily relies on the
network model, with graph neural networks (GNN) playing a crucial role in
network modeling. However, existing network modeling methods still lack a
comprehensive understanding of communication networks. In this paper, we
propose DWNet (Deeper and Wider Networks), a heterogeneous graph neural network
modeling method based on data-driven approaches that aims to address end-to-end
latency and jitter prediction in network models. This method stands out due to
two distinctive features: firstly, it introduces deeper levels of state
participation in the message passing process; secondly, it extensively
integrates relevant features during the feature fusion process. Through
experimental validation and evaluation, our model achieves higher prediction
accuracy compared to previous research achievements, particularly when dealing
with unseen network topologies during model training. Our model not only
provides more accurate predictions but also demonstrates stronger
generalization capabilities across diverse topological structures.
