Search Results for author: Kunqing Xie

Found 5 papers, 2 papers with code

GraphAD: A Graph Neural Network for Entity-Wise Multivariate Time-Series Anomaly Detection

no code implementations23 May 2022 Xu Chen, Qiu Qiu, Changshan Li, Kunqing Xie

We also construct a real-world entity-wise multivariate time-series dataset from the business data of Ele. me.

Anomaly Detection Management +2

Spatial-Temporal Graph ODE Networks for Traffic Flow Forecasting

1 code implementation24 Jun 2021 Zheng Fang, Qingqing Long, Guojie Song, Kunqing Xie

However, the representation ability of such models is limited due to: (1) shallow GNNs are incapable to capture long-range spatial correlations, (2) only spatial connections are considered and a mass of semantic connections are ignored, which are of great importance for a comprehensive understanding of traffic networks.

Ranked #7 on Traffic Prediction on PeMS07 (using extra training data)

Traffic Prediction

TrafficStream: A Streaming Traffic Flow Forecasting Framework Based on Graph Neural Networks and Continual Learning

1 code implementation11 Jun 2021 Xu Chen, Junshan Wang, Kunqing Xie

With the rapid growth of traffic sensors deployed, a massive amount of traffic flow data are collected, revealing the long-term evolution of traffic flows and the gradual expansion of traffic networks.

Continual Learning

Understanding and Improvement of Adversarial Training for Network Embedding from an Optimization Perspective

no code implementations17 May 2021 Lun Du, Xu Chen, Fei Gao, Kunqing Xie, Shi Han, Dongmei Zhang

Network Embedding aims to learn a function mapping the nodes to Euclidean space contribute to multiple learning analysis tasks on networks.

Link Prediction Network Embedding +1

TSSRGCN: Temporal Spectral Spatial Retrieval Graph Convolutional Network for Traffic Flow Forecasting

no code implementations30 Nov 2020 Xu Chen, Yuanxing Zhang, Lun Du, Zheng Fang, Yi Ren, Kaigui Bian, Kunqing Xie

Further analysis indicates that the locality and globality of the traffic networks are critical to traffic flow prediction and the proposed TSSRGCN model can adapt to the various temporal traffic patterns.


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