To optimize the graph pruning strategy in the TL-GPSTGN model, a multi-faceted approach can be employed. First, incorporating adaptive pruning techniques that dynamically adjust the pruning threshold based on the specific characteristics of the road network can enhance the model's ability to retain essential structural information while minimizing complexity. For instance, using a combination of Degree-based Pruning and Centrality-based Pruning can ensure that not only are less influential nodes removed, but also that critical nodes that play a significant role in traffic flow are preserved.
Additionally, implementing a feedback mechanism that evaluates the model's performance post-pruning can help refine the pruning criteria iteratively. By analyzing the impact of different pruning strategies on prediction accuracy, the model can learn to identify which nodes and edges are crucial for maintaining predictive performance.
Moreover, integrating machine learning techniques, such as reinforcement learning, could allow the model to learn optimal pruning strategies based on historical performance data. This would enable the model to adaptively balance the trade-off between preserving essential graph structure and reducing model complexity, ultimately leading to improved migration performance and predictive accuracy.