学术论文
1、Xiaocan Li, Kun Xie, Xin Wang, Gaogang Xie, DongliangXie, Zhenyu Li, Jigang Wen, Zulong Diao, Tian Wang, Quick and Accurate False Data Detection in Mobile Crowd Sensing, IEEE/ACM Transactions on Networking 2019 (ToN CCF-A类推荐期刊)
2、Kun Xie, Xiaocan Li(通信作者), XinWang, Gaogang Xie, Jigang Wen, Dafang Zhang, Active Sparse Mobile Crowd Sensing Based on Matrix Completion, ACM Conference on Management of Data（SIGMOD 2019 CCF-A类推荐会议）
3、Kun Xie, Xiaocan Li(通信作者), Xin Wang, Gaogang Xie, Dongliang Xie, Zhenyu Li,Jigang Wen, Zulong Diao, Quick and Accurate False Data Detection in Mobile Crowd Sensing, International Conference on Computer Communications (INFOCOM 2019 CCF-A类推荐会议)
4、Xiaocan Li, Kun Xie, Xin Wang, Gaogang Xie, JigangWen, Guangxing Zhang, Zheng Qin, Online Internet Anomaly Detection With High Accuracy: A Fast Tensor Factorization Solution, International Conference on Computer Communications (INFOCOM 2019 CCF-A类推荐会议)
5、Kun Xie, Xiaocan Li(通信作者),Xin Wang, Jiannong Cao, Gaogang Xie, Jigang Wen, Dafang Zhang, Zheng Qin, On-line Anomaly Detection with High Accuracy, IEEE/ACM Transactions on Networking,2018,(ToN CCF-A类推荐期刊)
6、Kun Xie, Xiaocan Li(通信作者) Xin Wang, Gaogang Xie, Jigang Wen, Dafang Zhang, Graph based Tensor Recovery For Accurate Internet Anomaly Detection, International Conference on Computer Communications (INFOCOM 2018 CCF-A类推荐会议)
7、Kun Xie, Xiaocan Li(通信作者), Xin Wang, Gaogang Xie,Jigang Wen, Jiannong Cao, Dafang Zhang, Fast Tensor Factorization for Accurate Internet Anomaly Detection, IEEE/ACM Transactions on Networking (ToN 2017 CCF-A类推荐期刊)
8、Xiaocan Li, Kun Xie, Xin Wang, Gaogang Xie, Kenli Li, Dafang Zhang, Jigang Wen, Order-preserved Tensor Completion For Accurate Network-wide Monitoring, IEEE/ACM International Symposium on Quality of Service (IWQoS 2022 CCF-B推荐会议)
9、Xiaocan Li, Kun Xie, Gaogang Xie, Kenli Li, Jiannong Cao, Dafang Zhang, Jigang Wen, Multi-view Matrix Factorization For Sparse Mobile Crowd Sensing, IEEE Internet of Things Journal（SCI 1区 IoTJ）
10、李晓灿,谢鲲,张大方,谢高岗,基于低秩分解的网络异常检测综述,计算机研究与发展 2022 （CCF-A推荐中文期刊）
11、Xiaocan Li, Kun Xie, Xin Wang, Gaogang Xie, Kenli Li, Jiannong Cao, Dafang Zhang, Jigang Wen, Tripartite Graph Aided Tensor Completion For Sparse Network Measurement, IEEE Transactions on Parallel and Distributed Systems（TPDS CCF-A推荐期刊）
12、Xiaocan Li, Kun Xie, Xin Wang, Gaogang Xie, Kenli Li, Jiannong Cao, Dafang Zhang, Hongbo Jiang, Jigang Wen, Neighbor Graph Based Tensor Recovery For Accurate Internet Anomaly Detection, IEEE Transactions on Parallel and Distributed Systems（TPDS CCF-A推荐期刊）
13、Cheng Wang, Kun Xie, Jiazheng Tian, Jigang Wen, Xiaocan Li, Gaogang Xie, Kenli Li, HPETC: History Priority Enhanced Tensor Completion For Network Distance Measurement, IEEE Transactions on Parallel and Distributed Systems（TPDS CCF-A推荐期刊）
14、Xiaocan Li, Kun Xie, Xin Wang, Gaogang Xie, Kenli Li, Jiannong Cao, Dafang Zhang, Jigang Wen, A Light-Weight and Robust Tensor Convolutional Autoencoder For Anomaly Detection, IEEE Transactions on Knowledge and Data Engineering, 2024, (TKDE CCF-A推荐期刊)
15、Kun Xie, Can Liu, Xin Wang, Xiaocan Li, Gaogang Xie, Jigang Wen, Kenli Li, Nerual Network Compression Based on Tensor Ring Decomposition, IEEE Transactions on Neural Networks and Learning Systems (TNNLS), 2024, SCI一区
16、Xiaocan Li, Kun Xie, Jigang Wen, Gaogang Xie, Wei Liang, Tensor Factorization for Accurate Anomaly Detection in Dynamic Networks, IEEE Transactions on Sustainable Computing, 2024, (SCI)
17、 Qixue Lin, Xiaocan Li（共一）, Kun Xie, Jigang Wen, Shiming He, Gaogang Xie, Xiaopeng Fan, Quan Feng, Network Monitoring Data Recovery Based on Flexible Bi-direction Model, IEEE Transactions on Network Science and Engineering, 2025
18、 Jigang Wen, Xiaocan Li（共一）, Kun Xie, Wei Liang, Gaogang Xie, J-Tucker: Joint Compression Scheme for EfficientDeployment of Multi-Task Deep Learning Modelson Edge Devices, lEEE Network Magazine, 2025
19、Xiaocan Li, Kun Xie, Jigang Wen, Guangxing Zhang, Wei Liang, Gaogang Xie, Kenli Li, Joint Neural Matrix Completion for Multi-Attribute Mobile Crowd Sensing, INFOCOM 2025 (计算机学会CCF A类会议)
科研状况
