GNN Evaluation

Evaluating the performance of Graph Neural Networks (GNNs) is a crucial but challenging task, particularly when dealing with unseen, unlabeled graphs common in real-world applications. Current research focuses on developing robust evaluation methods that account for variations in graph structure and data distribution between training and testing sets, often employing techniques to estimate performance metrics without relying on ground truth labels. These advancements are vital for ensuring the reliable deployment of GNNs across diverse applications, improving the reproducibility and comparability of research findings within the field. The ultimate goal is to provide more accurate and consistent performance assessments, leading to more trustworthy and effective GNN models.