9 papers
TAGFN: A Text-Attributed Graph Dataset for Fake News Detection in the Age of LLMs
Kay Liu, Yuwei Han, Haoyan Xu +3
Large Language Models (LLMs) have recently revolutionized machine learning on text-attributed graphs, but the application of LLMs to graph outlier detection, particularly in the co…
Topology-Aware Conformal Prediction for Stream Networks
Jifan Zhang, Fangxin Wang, Zihe Song +3
Stream networks, a unique class of spatiotemporal graphs, exhibit complex directional flow constraints and evolving dependencies, making uncertainty quantification a critical yet c…
FedGraph: A Research Library and Benchmark for Federated Graph Learning
Yuhang Yao, Yuan Li, Xinyi Fan +7
Federated graph learning is an emerging field with significant practical challenges. While algorithms have been proposed to improve the accuracy of training graph neural networks,…
Uncertainty in Graph Neural Networks: A Survey
Fangxin Wang, Yuqing Liu, Kay Liu +3
Graph Neural Networks (GNNs) have been extensively used in various real-world applications. However, the predictive uncertainty of GNNs stemming from diverse sources such as inhere…
Enhancing Fairness in Unsupervised Graph Anomaly Detection through Disentanglement
Wenjing Chang, Kay Liu, Philip S. Yu +1
Graph anomaly detection (GAD) is increasingly crucial in various applications, ranging from financial fraud detection to fake news detection. However, current GAD methods largely o…
Confidence-aware Fine-tuning of Sequential Recommendation Systems via Conformal Prediction
Chen Wang, Fangxin Wang, Ruocheng Guo +2
In Sequential Recommendation Systems (SRecsys), traditional training approaches that rely on Cross-Entropy (CE) loss often prioritize accuracy but fail to align well with user sati…