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Preprint
Kai Wu, “Intelligent Modeling Algorithm for Complex System and its Application (复杂系统智能建模算法及其应用研究),” Doctoral Dissertation, DOI:10.27389/d.cnki.gxadu.2020.0001602020.
Kai Wu, Chao Wang, Jing Liu, “Network Collaborator: Knowledge Transfer Between Network Reconstruction and Community Detection,” Neurocomputing, Submitted, 2024.
Shanchao Yang, Jing Liu, Kai Wu, Mingming Li, “Learn to generate time series conditioned graphs with generative adversarial nets,” arXiv preprint arXiv:2003.01436, 2020.
Journal Papers
2024
2023
Yuanyuan Li, Kai Wu*, Jing Liu, “Discover governing differential equations from evolving systems,” Physical Review Research, vol. 5, 023126, 2023. (code)
Huixin Ma, Kai Wu*, Handing Wang, Jing Liu, “Higher-order Knowledge Transfer for Dynamic Community Detection with Great Changes,” IEEE Transactions on Evolutionary Computation, vol. 28, no. 1, pp. 90-104, 2024. (code)
Yitong Li, Kai Wu*, Jing Liu, “Self-paced ARIMA for Robust Time Series Prediction,” Knowledge-Based Systems, vol. 269, pp. 110489, 2023.
Yilu Liu, Jing Liu, Kai Wu, “Cost-Effective Competition on Social Networks via Pareto Optimization,” Information Sciences, vol. 620, pp. 31-46, 2023.
Chao Wang, Jiaxuan Zhao, Lingling Li, Licheng Jiao, Jing Liu, Kai Wu, “A Multi-Transformation Evolutionary Framework for Influence Maximization in Social Networks,” IEEE Computational Intelligence Magazine, vol. 18, no. 1, pp. 52-67, 2023. (code)
2022
Kai Wu, Chao Wang*, Jing Liu, “Evolutionary multitasking multilayer network reconstruction,” IEEE Transactions on Cyberentics, vol. 52, no. 12, pp. 12854-12868, 2022. (code)
Kai Wu, Chao Wang, Jing Liu, “Multilayer nonlinear dynamical network reconstruction from streaming data,” SCIENTIA SINICA Technologica, vol. 52, no. 6, pp. 971-982, 2022. (code)
Kai Wu, Xingxing Hao*, Jing Liu, Penghui Liu, Fang Shen, “Online reconstruction of complex networks from streaming data,” IEEE Transactions on Cybernetics, vol. 52, no. 6, pp. 5136-5147, 2022. (code)
Kaixin Yuan, Kai Wu*, Jing Liu, “Is Single Enough? A Joint Spatiotemporal Feature Learning Framework for Multivariate Time Series Prediction,” IEEE Transactions on Neural networks and Learning Systems, vol. 35, no. 4, pp. 4985-4998, 2024. (code)
Kai Wu, Jing Liu*, “Learning large-scale fuzzy cognitive maps under limited resources,” Engineering Applications of Artificial Intelligence, vol. 116, pp. 105376, 2022.(code)
Kai Wu, Kaixin Yuan*, Yingzhi Teng, Jing Liu, Licheng Jiao, “Broad fuzzy cognitive map systems for time series classification,” Applied Soft Computing, vol. 128, pp. 109458, 2022.(code)
李怡桐, 刘晓涛, 刘静, 吴凯, “基于加权局部复杂不变性的时间序列分类算法,” 系统仿真学报, DOI: 10.16182/j.issn1004731x.joss.21-0456, 2022, In Press.
2021
Kai Wu, Jing Liu*, Xingxing Hao, Penghui Liu, Fang Shen, “An evolutionary multi-objective framework for complex network reconstruction using community structure,” IEEE Transactions on Evolutionary Computation, vol. 25, no. 2, pp. 247-261, 2021. (code)
Kai Wu, Jing Liu*, Penghui Liu, Fang Shen, “Online fuzzy cognitive map learning,” IEEE Transactions on Fuzzy Systems, vol. 29, no. 7, pp. 1885-1898, 2021. (code)
Kai Wu, Jing Liu*, Penghui Liu, Shanchao Yang, “Time series prediction using sparse autoencoder and high-order fuzzy cognitive maps,” IEEE Transactions on Fuzzy Systems, vol. 28, no. 12, pp. 3110-3121, 2021. (code)
Chao Wang, Kai Wu*, Jing Liu, “Evolutionary Multitasking AUC optimization,” IEEE Computational Intelligence Magazine, vol. 17, no. 2, pp. 67-82, 2022. (code)
Chaolong Ying, Jing Liu*, Kai Wu, Chao, Wang, “A multiobjective evolutionary approach for solving large-scale network reconstruction problems via logistic principal component analysis,” IEEE Transactions on Cyberentics, vol. 53, no. 4, pp. 2137-2510, 2023. (code)
Fang Shen, Jing Liu*, Kai Wu, “Evolutionary Multitasking network reconstruction from time series with online parameter estimation,” Knowledge-based Systems, vol. 222, 107019, 2021.
Chao Wang, Jing Liu*, Kai Wu, Zhaoyang Wu, “Solving multi-task optimization problems with adaptive knowledge transfer via anomaly detection,” IEEE Transactions on Evolutionary Computation, vol. 26, no. 2, pp. 304-318, 2022. (code)
Kaixin Yuan, Jing Liu*, Shanchao Yang, Kai Wu, Fang Shen, “Time series forecasting based on kernel mapping and high-order fuzzy cognitive maps,” Knowledge-based Systems, vol. 206, 106359, 2020. (code)
Fang Shen, Jing Liu*, Kai Wu, “Multivariate time series forecasting based on elastic net and high-order fuzzy cogitive maps: A case study on human action prediction through EEG signals,” IEEE Transactions on Fuzzy Systems, vol. 29, no. 8, pp. 2336-2348, 2021.
2020
Fang Shen, Jing Liu*, Kai Wu, “A preference-based evolutionary bi-objective approach for learning large-scale fuzzy cognitive maps: An application to gene regulatory network reconstruction,” IEEE Transactions on Fuzzy Systems, vol. 28, no. 6, pp. 1035-1049, 2020.
Penghui Liu, Jing Liu*, Kai Wu, “CNN-FCM: system modeling promotes stablity of deep learning in time series prediction,” Knowledge-based Systems, vol. 203, 106081, 2020.
Fang Shen, Jing Liu*, Kai Wu, “Evolutionary multitasking fuzzy cognitive map learning,” Knowledge-Based Systems, vol. 192, pp. 105294, 2020.
2016-2019
Kai Wu, Jing Liu*, Dan Chen, “Network reconstruction based on time series via memetic algorithm,” Knowledge-Based Systems, vol. 164, pp. 404-425, 2019.
Kai Wu, Jing Liu*, “Learning large-scale fuzzy cognitive maps based on compressed sensing and application in reconstructing gene regulatory networks,” IEEE Transactions on Fuzzy Systems, vol. 25, no. 6, pp. 1546-1560, 2017.
Kai Wu, Jing Liu*, Yaxiong Chi, “Wavelet fuzzy cognitive maps,” Neurocomputing, vol. 232, pp. 94-103, 2017.
Kai Wu, Jing Liu*, Shuai Wang, “Reconstructing networks from profit sequences in evolutionary games via a multiobjective optimization approach with lasso initialization,” Scientific Reports, vol. 6, pp. 37771, 2016.
Kai Wu, Jing Liu*, “Robust learning of large-scale fuzzy cognitive maps via the lasso from noisy time series,” Knowledge-Based Systems, vol. 113, pp. 23-38, 2016.
Zhangtao Li, Jing Liu*, Kai Wu, “A multiobjective evolutionary algorithm based on structural and attribute similarities for community detection in attributed networks,” IEEE Transactions on Cybernetics, vol. 48, no. 7, pp.1963-1976, 2018.
Conference Papers
Yudong Yang, Kai Wu*, Xiangyi Teng, Handing Wang, He Yu, Jing Liu, “Exploring Knowledge Transfer in Evolutionary Many-task Optimization: A Complex Network Perspective,” GECCO 2024.
Lanlan Chen, Kai Wu*, Jing Liu, “Signed Graph Neural Ordinary Differential Equation for Modeling Continuous-time Dynamics,” AAAI 2024, vol.38, no.8, pp. 8292-8301. (code)
Kai Wu, Xiangyi Teng*, Jing Liu, “Locating hidden sources in evolutionary games based on fuzzy cognitive map,” ChineseCSCW '21: Proceedings of the 16th Chinese Conference on Computer Supported Cooperative Work and Social Computing accepted
Kai Wu, Jing Liu*, Chao Wang, Kaixin Yuan, “Pareto optimization for influence maximization in social networks,” EMO2021, Shenzhan, China, 2021, pp. 697-707. (code)
Kai Wu, Jing Liu*, “Multi-objective evolutionary top rank optimization with Pareto ensemble,” Proceedings of 2020 IEEE Symposium Series on Computational Intelligence (IEEE SSCI2020), Canberra, ACT, Australia, 2020, pp. 624-630.
Kai Wu, Jing Liu*, “Classification-based optimization with multi-fidelity evaluations,” Proceedings of IEEE Congress on Evolutionary Computation 2019 (IEEE CEC2019), Wellington, New Zealand, 2019, pp. 1126-1131.
Kai Wu, Jing Liu*, “Evolutionary game network reconstruction by memetic algorithm with l1/2 regularization,” 2017 Asia-Pacific Conference on Simulated Evolution and Learning, Shenzhen, China, 2017, pp. 385-396.
Kai Wu, Jing Liu*, “Learning of sparse fuzzy cognitive maps using evolutionary algorithm with lasso initialization,” 2017 Asia-Pacific Conference on Simulated Evolution and Learning, Shenzhen, China, 2017, pp. 966-973.
Ze Yang, Jing Liu*, Kai Wu, “Learning of boosting fuzzy cognitive maps using a real-coded genetic algorithm,” Proceedings of IEEE Congress on Evolutionary Computation 2019 (IEEE CEC2019), Wellington, New Zealand, 2019, PP. 490-498.
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