Case Study: Evaluating GNN Performance Using Shortest-Path Distance for Generalization and Fairness | HackerNoon
Briefly

from Hackernoon7 months ago
In our case study, we demonstrate the practical applications of our proposed framework by validating the effectiveness of Graph Neural Networks (GNNs) using shortest-path distance metrics to enhance understanding of their generalization capabilities over structural subgroups.
Hackernoonhttps://hackernoon.com/case-study-evaluating-gnn-performance-using-shortest-path-distance-for-generalization-and-fairness
The findings from our case study indicate that our framework can not only assess GNN performance on specific structures but also offers insights applicable to tackling real-world challenges, particularly in improving graph active learning strategies.
Hackernoonhttps://hackernoon.com/case-study-evaluating-gnn-performance-using-shortest-path-distance-for-generalization-and-fairness
Read at Hackernoon
#graph-neural-networks #generalization-performance #real-world-applications #cold-start-problem #graph-active-learning
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