1 citations · 1 across the 7 of their papers we have counts for
8 papers
Bayesian Membership Privacy for Graph Neural Networks
Sinan Yıldırım, Megha Khosla
Existing privacy analyses for Graph Neural Networks (GNNs) largely inherit assumptions from non-graph settings, overlooking structural correlations and stochastic training-graph sa…
Impact of Graph Structure on Membership-Inference Risk for Graph Neural Networks
Megha Khosla
Graph neural networks (GNNs) are widely used for tasks such as node classification and link prediction, but their use in sensitive settings raises concerns about training-data leak…
Draw a Portrait of Your Graph Data: An Instance-Level Profiling Framework for Graph-Structured Data
Tianqi Zhao, Russa Biswas, Megha Khosla
Graph machine learning models often achieve similar overall performance yet behave differently at the node level, failing on different subsets of nodes with varying reliability. St…
Population-Scale Network Embeddings Expose Educational Divides in Network Structure Related to Right-Wing Populist Voting
Malte Lüken, Javier Garcia-Bernardo, Sreeparna Deb +2
Administrative registry data can be used to construct population-scale networks whose ties reflect shared social contexts between persons. With machine learning, such networks can…
GNN-MultiFix: Addressing the pitfalls for GNNs for multi-label node classification
Tianqi Zhao, Megha Khosla
Graph neural networks (GNNs) have emerged as powerful models for learning representations of graph data showing state of the art results in various tasks. Nevertheless, the superio…
A data-centric approach for assessing progress of Graph Neural Networks
Tianqi Zhao, Ngan Thi Dong, Alan Hanjalic +1
Graph Neural Networks (GNNs) have achieved state-of-the-art results in node classification tasks. However, most improvements are in multi-class classification, with less focus on t…
AGALE: A Graph-Aware Continual Learning Evaluation Framework
Tianqi Zhao, Alan Hanjalic, Megha Khosla
In recent years, continual learning (CL) techniques have made significant progress in learning from streaming data while preserving knowledge across sequential tasks, particularly…
Model Selection with Model Zoo via Graph Learning
Ziyu Li, Hilco van der Wilk, Danning Zhan +3
Pre-trained deep learning (DL) models are increasingly accessible in public repositories, i.e., model zoos. Given a new prediction task, finding the best model to fine-tune can be…