9 citations · 20 across the 7 of their papers we have counts for
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cs.LG2023
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…
cs.LG2023★ 1 cited
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…
cs.LG2023★ 4 cited
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…