2 papers
cs.LG2025
UMGAD: Unsupervised Multiplex Graph Anomaly Detection
Xiang Li, Jianpeng Qi, Zhongying Zhao +4
Graph anomaly detection (GAD) is a critical task in graph machine learning, with the primary objective of identifying anomalous nodes that deviate significantly from the majority.…
cs.LG2024
How Much Can Time-related Features Enhance Time Series Forecasting?
Chaolv Zeng, Yuan Tian, Guanjie Zheng +1
Recent advancements in long-term time series forecasting (LTSF) have primarily focused on capturing cross-time and cross-variate (channel) dependencies within historical data. Howe…