The proposed personalized scoping approach can be effectively extended to other graph-based tasks, such as link prediction, by adapting the scope prediction mechanism to focus on the relationships between node pairs rather than individual nodes. In link prediction, the goal is to predict the existence of edges between nodes based on their features and structural patterns. By leveraging the same principles of personalized scoping, we can develop a model that predicts the optimal depth of a GNN for each node pair based on their local structural context.
To implement this, the scope predictor can be modified to consider the features and connections of both nodes in a pair, allowing it to learn the most relevant structural patterns that influence the likelihood of a link. This could involve encoding the joint structural patterns of the two nodes, such as their common neighbors and the homophily or heterophily characteristics of their connections. Additionally, the model can utilize the logits from multiple GNN depths to assess how well different depths capture the relationship dynamics between the nodes. By integrating these elements, the personalized scoping approach can enhance link prediction performance by tailoring the receptive field to the specific structural context of each node pair.