The current scope prediction model, while effective, has several potential limitations. One significant limitation is its reliance on local structural patterns, which may not fully capture the complexities of global graph structures. For instance, in highly interconnected or dynamic graphs, the relationships between nodes can be influenced by broader structural patterns that are not adequately represented by local features alone.
To improve the model's ability to handle more complex structural patterns, several strategies can be employed. First, incorporating multi-scale structural information could enhance the model's understanding of both local and global contexts. This could involve using hierarchical graph representations or attention mechanisms that allow the model to focus on relevant substructures across different scales.
Second, integrating temporal dynamics into the scope prediction could provide insights into how relationships evolve over time, which is particularly important in dynamic graphs. By considering the temporal aspect of node interactions, the model can better adapt its predictions to changing structural patterns.
Lastly, enhancing the feature fusion process to include more diverse modalities, such as node embeddings from pre-trained models or additional graph-theoretic metrics, could provide richer information for the scope predictor. This would enable the model to learn more nuanced relationships and improve its generalization capabilities across various graph structures.