E2NGD: A Graph Layout Algorithm Based on Edge-Node Message Passing*

Many existing studies employ graph neural networks (GNNs) to aggregate information from neighboring nodes and learn node embeddings in a low-dimensional space, aiming to produce layouts that satisfy established aesthetic criteria. However, current GNN approaches for graph layout tasks primarily focus on node features, often neglecting the complex relationships between edge features. To address this limitation and enhance the model's ability to capture complex graph structures, we propose a plug-and-play edge message passing enhancement module that can be easily integrated into existing layout frameworks. This module incorporates both edge distance features and angular relationships between edges, enabling more effective information exchange. We evaluate the usability and effectiveness of this module through quantitative and qualitative experiments.