4 papers
PaSta: Noisy Node Classification with Partial Label Learning
Yujing Liu, Yixin Liu, Yu Zheng +3
Noisy node classification problem is a fundamental yet challenging task for real-world graph-related web services, where node labels are often corrupted or unreliable due to weak s…
Rethinking Feature Alignment in Generalist Graph Anomaly Detection: A Relational Fingerprint-based Approach
Yujing Liu, Yixin Liu, Yu Zheng +3
Generalist graph anomaly detection (GAD) aims to detect anomalies on unseen graphs without graph-specific retraining. Nevertheless, existing approaches primarily focus on aligning…
Noisy Node Classification by Bi-level Optimization based Multi-teacher Distillation
Yujing Liu, Zongqian Wu, Zhengyu Lu +4
Previous graph neural networks (GNNs) usually assume that the graph data is with clean labels for representation learning, but it is not true in real applications. In this paper, w…
Adaptive Multi-Modality Prompt Learning
Zongqian Wu, Yujing Liu, Mengmeng Zhan +3
Although current prompt learning methods have successfully been designed to effectively reuse the large pre-trained models without fine-tuning their large number of parameters, the…