9 citations · 20 across the 7 of their papers we have counts for
7 papers
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…
Fine-Grained Population Mobility Data-Based Community-Level COVID-19 Prediction Model
Pengyue Jia, Ling Chen, Dandan Lyu
Predicting the number of infections in the anti-epidemic process is extremely beneficial to the government in developing anti-epidemic strategies, especially in fine-grained geogra…
Multi-Scale Adaptive Graph Neural Network for Multivariate Time Series Forecasting
Ling Chen, Donghui Chen, Zongjiang Shang +4
Multivariate time series (MTS) forecasting plays an important role in the automation and optimization of intelligent applications. It is a challenging task, as we need to consider…
Scale-Aware Neural Architecture Search for Multivariate Time Series Forecasting
Donghui Chen, Ling Chen, Zongjiang Shang +3
Multivariate time series (MTS) forecasting has attracted much attention in many intelligent applications. It is not a trivial task, as we need to consider both intra-variable depen…
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…