Despite its advantages, NBA-GNN has potential limitations that warrant consideration. One significant limitation is the increased computational complexity associated with maintaining separate hidden features for each edge, which may lead to higher memory usage and slower training times, especially in large graphs. Future research could focus on optimizing the memory footprint of NBA-GNN by exploring techniques such as feature sharing or dimensionality reduction to mitigate the overhead of storing multiple edge features.
Another limitation is the potential loss of information due to the strict non-backtracking constraint, which may hinder the model's ability to capture certain graph structures where backtracking could provide valuable context. To address this, future work could investigate hybrid approaches that allow controlled backtracking in specific scenarios, thereby balancing the benefits of non-backtracking with the richness of information that backtracking can provide.
Additionally, the performance of NBA-GNN may vary across different types of graphs, particularly in highly dynamic or heterogeneous graphs. Future research could explore the adaptability of NBA-GNN to various graph types by incorporating mechanisms that allow the model to learn when to apply non-backtracking updates versus traditional message-passing methods.