8 citations · 9 across the 3 of their papers we have counts for
4 papers · 1 filter
Online GNN Evaluation Under Test-time Graph Distribution Shifts
Xin Zheng, Dongjin Song, Qingsong Wen +2
Evaluating the performance of a well-trained GNN model on real-world graphs is a pivotal step for reliable GNN online deployment and serving. Due to a lack of test node labels and…
GOODAT: Towards Test-time Graph Out-of-Distribution Detection
Luzhi Wang, Dongxiao He, He Zhang +5
Graph neural networks (GNNs) have found widespread application in modeling graph data across diverse domains. While GNNs excel in scenarios where the testing data shares the distri…
GraphGuard: Detecting and Counteracting Training Data Misuse in Graph Neural Networks
Bang Wu, He Zhang, Xiangwen Yang +4
The emergence of Graph Neural Networks (GNNs) in graph data analysis and their deployment on Machine Learning as a Service platforms have raised critical concerns about data misuse…
GNNEvaluator: Evaluating GNN Performance On Unseen Graphs Without Labels
Xin Zheng, Miao Zhang, Chunyang Chen +3
Evaluating the performance of graph neural networks (GNNs) is an essential task for practical GNN model deployment and serving, as deployed GNNs face significant performance uncert…