8 citations · 14 across the 5 of their papers we have counts for
5 papers
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…
Securing Graph Neural Networks in MLaaS: A Comprehensive Realization of Query-based Integrity Verification
Bang Wu, Xingliang Yuan, Shuo Wang +3
The deployment of Graph Neural Networks (GNNs) within Machine Learning as a Service (MLaaS) has opened up new attack surfaces and an escalation in security concerns regarding model…
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…