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cs.LG2025
DeNoise: Learning Robust Graph Representations for Unsupervised Graph-Level Anomaly Detection
Qingfeng Chen, Haojin Zeng, Jingyi Jie +2
With the rapid growth of graph-structured data in critical domains, unsupervised graph-level anomaly detection (UGAD) has become a pivotal task. UGAD seeks to identify entire graph…
cs.LG2025
Toward Fair Graph Neural Networks Via Dual-Teacher Knowledge Distillation
Chengyu Li, Debo Cheng, Guixian Zhang +2
Graph Neural Networks (GNNs) have demonstrated strong performance in graph representation learning across various real-world applications. However, they often produce biased predic…
cs.LG2024
Community-Centric Graph Unlearning
Yi Li, Shichao Zhang, Guixian Zhang +1
Graph unlearning technology has become increasingly important since the advent of the `right to be forgotten' and the growing concerns about the privacy and security of artificial…