A journal of IEEE and CAA , publishes high-quality papers in English on original theoretical/experimental research and development in all areas of automation

IEEE/CAA Journal of Automatica Sinica

  • JCR Impact Factor: 15.3, Top 1 (SCI Q1)
    CiteScore: 23.5, Top 2% (Q1)
    Google Scholar h5-index: 77, TOP 5
Turn off MathJax
Article Contents
Y. Shi and Z. Liu, “A multi-constrained matrix factorization approach for community detection relying on alternating-direction-method of multipliers,” IEEE/CAA J. Autom. Sinica, vol. 11, no. 0, pp. 1–3, Oct. 2023.
Citation: Y. Shi and Z. Liu, “A multi-constrained matrix factorization approach for community detection relying on alternating-direction-method of multipliers,” IEEE/CAA J. Autom. Sinica, vol. 11, no. 0, pp. 1–3, Oct. 2023.

A Multi-Constrained Matrix Factorization Approach for Community Detection Relying on Alternating-Direction-Method of Multipliers

More Information
  • loading
  • [1]
    L. Hu, S. Yang, X. Luo, H. Yuan, K. Sedraoui, and M. Zhou, “A distributed framework for large-scale protein-protein interaction data analysis and prediction using MapReduce,” IEEE/CAA J. Autom. Sinica, vol. 9, no. 1, pp. 160–172, Jan. 2022. doi: 10.1109/JAS.2021.1004198
    [2]
    Y. Zhou, X. Luo, and M. Zhou, “Cryptocurrency transaction network embedding from static and dynamic perspectives: An overview,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 5, pp. 1105–1121, May 2023. doi: 10.1109/JAS.2023.123450
    [3]
    X. Luo, W. Qin, A. Dong, K. Sedraoui, and M. Zhou, “Efficient and high-quality recommendations via momentum-incorporated parallel stochastic gradient descent-based learning,” IEEE/CAA J. Autom. Sinica, vol. 8, no. 2, pp. 402–411, Feb. 2021. doi: 10.1109/JAS.2020.1003396
    [4]
    D. Jin, et al., “A survey of community detection approaches: From statistical modeling to deep learning,” IEEE Trans. Knowledge and Data Engineering, vol. 35, no. 2, pp. 1149–1170, Feb. 2023.
    [5]
    X. Su et al. , “A comprehensive survey on community detection with deep learning,” IEEE Trans. on Neural Networks and Learning Systems.
    [6]
    C. He, X. Fei, Q. Cheng, H. Li, Z. Hu, and Y. Tang, “A survey of community detection in complex networks using nonnegative matrix factorization,” IEEE Trans. Computational Social Systems, vol. 9, no. 2, pp. 440–457, Apr. 2022. doi: 10.1109/TCSS.2021.3114419
    [7]
    B. Sun, H. Shen, J. Gao, W. Ouyang, and X. Cheng, “A nonnegative symmetric encoder-decoder approach for community detection,” in Proc. ACM on Conf. Information and Knowledge Management, Singapore, Nov. 2017, pp. 597–606.
    [8]
    X. Ma, D. Dong, and Q. Wang, “Community detection in multi-layer networks using joint nonnegative matrix factorization,” IEEE Trans. Knowledge and Data Engineering, vol. 31, no. 2, pp. 273–286, 2019. doi: 10.1109/TKDE.2018.2832205
    [9]
    C. Leng, H. Zhang, G. Cai, I. Cheng, and A. Basu, “Graph regularized Lp smooth non-negative matrix factorization for data representation,” IEEE/CAA J. Autom. Sinica, vol. 6, no. 2, pp. 584–595, 2019. doi: 10.1109/JAS.2019.1911417
    [10]
    L. Yang, X. Cao, D. Jin, X. Wang, and D. Meng, “A unified semi-supervised community detection framework using latent space graph regularization,” IEEE Trans. Cyber., vol. 45, no. 11, pp. 2585–2598, 2015. doi: 10.1109/TCYB.2014.2377154
    [11]
    F. Ye, C. Chen, Z. Wen, Z. Zheng, W. Chen, and Y. Zhou, “Homophily preserving community detection,” IEEE Trans. Neural Networks and Learning Systems, vol. 31, no. 8, pp. 2903–2915, Aug. 2020. doi: 10.1109/TNNLS.2019.2933850
    [12]
    X. Luo, Z. Liu, L. Jin, Y. Zhou, and M. Zhou, “Symmetric nonnegative matrix factorization-based community detection models and their convergence analysis,” IEEE Trans. Neural Networks and Learning Systems, vol. 33, no. 3, pp. 1203–1215, Mar. 2022. doi: 10.1109/TNNLS.2020.3041360
    [13]
    D. Kuang, S. Yun, and H. Park, “SymNMF: Nonnegative low-rank approximation of a similarity matrix for graph clustering,” J. Global Optimization, vol. 62, no. 3, pp. 545–574, Jul. 2015. doi: 10.1007/s10898-014-0247-2
    [14]
    X. Li, Z. Zhu, Q. Li, and K. Liu, “A provable splitting approach for symmetric nonnegative matrix factorization,” IEEE Trans. Knowledge and Data Engineering, vol. 35, no. 3, pp. 2206–2219, Mar. 2023.
    [15]
    F. Bi, X. Luo, B. Shen, H. Dong, and Z. Wang, “Proximal alternating-direction-method-of-multipliers-incorporated nonnegative latent factor analysis,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 6, pp. 1388–1406, Jun. 2023. doi: 10.1109/JAS.2023.123474
    [16]
    D. Wu and X. Luo, “Robust latent factor analysis for precise representation of high-dimensional and sparse data,” IEEE/CAA J. Autom. Sinica, vol. 8, no. 4, pp. 796–805, Apr. 2021. doi: 10.1109/JAS.2020.1003533
    [17]
    H. Wu, X. Luo, M. Zhou, M. J. Rawa, K. Sedraoui, and A. Albeshri, “A PID-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis,” IEEE/CAA J. Autom. Sinica, vol. 9, no. 3, pp. 533–546, Mar. 2022. doi: 10.1109/JAS.2021.1004308
    [18]
    X. Wang, P. Cui, J. Wang, J. Pei, W. Zhu, and S. Yang, “Community preserving network embedding,” in Proc. 30st AAAI Conf. Artificial Intelligence, San Francisco, USA, 2017, pp. 203–209.
    [19]
    J. Leskovec and R. Sosic, “SNAP: A general-purpose network analysis and graph-mining library,” ACM Trans. Intelligent Systems and Technology, vol. 8, no. 1, p. 1, Jun. 2016.
    [20]
    D. Cai, X. He, J. Han, and T. Huang, “Graph regularized nonnegative matrix factorization for data representation,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 33, no. 8, pp. 1548–1560, Aug. 2011. doi: 10.1109/TPAMI.2010.231

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索

    Figures(1)  / Tables(4)

    Article Metrics

    Article views (100) PDF downloads(18) Cited by()

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return