4 papers · 1 filter
HRGCN: Heterogeneous Graph-level Anomaly Detection with Hierarchical Relation-augmented Graph Neural Networks
Jiaxi Li, Guansong Pang, Ling Chen +1
This work considers the problem of heterogeneous graph-level anomaly detection. Heterogeneous graphs are commonly used to represent behaviours between different types of entities i…
Graph-level Anomaly Detection via Hierarchical Memory Networks
Chaoxi Niu, Guansong Pang, Ling Chen
Graph-level anomaly detection aims to identify abnormal graphs that exhibit deviant structures and node attributes compared to the majority in a graph set. One primary challenge is…
DSTCGCN: Learning Dynamic Spatial-Temporal Cross Dependencies for Traffic Forecasting
Binqing Wu, Ling Chen
Traffic forecasting is essential to intelligent transportation systems, which is challenging due to the complicated spatial and temporal dependencies within a road network. Existin…
Learning from Multiple Time Series: A Deep Disentangled Approach to Diversified Time Series Forecasting
Ling Chen, Weiqi Chen, Binqing Wu +3
Time series forecasting is a significant problem in many applications, e.g., financial predictions and business optimization. Modern datasets can have multiple correlated time seri…