Publications (5)
AnomalyLLM: Few-shot Anomaly Edge Detection for Dynamic Graphs using Large Language Models
Shuo Liu, Di Yao, Lanting Fang +5
Detecting anomaly edges for dynamic graphs aims to identify edges significantly deviating from the normal pattern and can be applied in various domains, such as cybersecurity, fina…
Learning the Relation between Similarity Loss and Clustering Loss in Self-Supervised Learning
Jidong Ge, Yuxiang Liu, Jie Gui +5
Self-supervised learning enables networks to learn discriminative features from massive data itself. Most state-of-the-art methods maximize the similarity between two augmentations…
Invariant Graph Representations for Continuous-Time Dynamic Graphs Under Distribution Shifts
Lanting Fang, Yulian Yang, Yawei Zhang +3
Continuous-Time Dynamic Graphs (CTDGs) enable fine-grained modeling of evolving relational systems. However, most existing CTDG representation learning methods are tailored to in-d…
Backdooring Self-Supervised Contrastive Learning by Noisy Alignment
Tuo Chen, Jie Gui, Minjing Dong +3
Self-supervised contrastive learning (CL) effectively learns transferable representations from unlabeled data containing images or image-text pairs but suffers vulnerability to dat…
PORCA: Root Cause Analysis with Partially Observed Data
Chang Gong, Di Yao, Jin Wang +6
Root Cause Analysis (RCA) aims at identifying the underlying causes of system faults by uncovering and analyzing the causal structure from complex systems. It has been widely used…