A journal of IEEE and CAA , publishes high-quality papers in English on original theoretical/experimental research and development in all areas of automation
Volume 9 Issue 3
Mar.  2022

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
H. Wu, X. Luo, M. C. 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
Citation: H. Wu, X. Luo, M. C. 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

A PID-incorporated Latent Factorization of Tensors Approach to Dynamically Weighted Directed Network Analysis

doi: 10.1109/JAS.2021.1004308
Funds:  This work was supported in part by the National Natural Science Foundation of China (61772493), the CAAI-Huawei MindSpore Open Fund (CAAIXSJLJJ-2020-004B), in part by the Natural Science Foundation of Chongqing of China (cstc2019jcyjjqX0013), in part by the Pioneer Hundred Talents Program of Chinese Academy of Sciences, and in part by the Deanship of Scientific Research (DSR) at King Abdulaziz University, Jeddah, Saudi Arabia (FP-165-43)
More Information
  • A large-scale dynamically weighted directed network (DWDN) involving numerous entities and massive dynamic interaction is an essential data source in many big-data-related applications, like in a terminal interaction pattern analysis system (TIPAS). It can be represented by a high-dimensional and incomplete (HDI) tensor whose entries are mostly unknown. Yet such an HDI tensor contains a wealth knowledge regarding various desired patterns like potential links in a DWDN. A latent factorization-of-tensors (LFT) model proves to be highly efficient in extracting such knowledge from an HDI tensor, which is commonly achieved via a stochastic gradient descent (SGD) solver. However, an SGD-based LFT model suffers from slow convergence that impairs its efficiency on large-scale DWDNs. To address this issue, this work proposes a proportional-integral-derivative (PID)-incorporated LFT model. It constructs an adjusted instance error based on the PID control principle, and then substitutes it into an SGD solver to improve the convergence rate. Empirical studies on two DWDNs generated by a real TIPAS show that compared with state-of-the-art models, the proposed model achieves significant efficiency gain as well as highly competitive prediction accuracy when handling the task of missing link prediction for a given DWDN.

     

  • loading
  • [1]
    V. Martínez, F. Berzal, and J. C. Cubero, “A survey of link prediction in complex networks,” ACM Comput. Surv., vol. 49, no. 4, Article No. 69, Feb. 2016.
    [2]
    X. Luo, M. C. Zhou, S. Li, D. Wu, Z. G. Liu, and M. S. Shang, “Algorithms of unconstrained non-negative latent factor analysis for recommender systems,” IEEE Trans. Big Data, vol. 7, no. 1, pp. 227–240, Mar. 2021. doi: 10.1109/TBDATA.2019.2916868
    [3]
    Y. Song, M. Li, X. Luo, G. S. Yang, and C. J. Wang, “Improved symmetric and nonnegative matrix factorization models for undirected, sparse and large-scaled networks: A triple factorization-based approach,” IEEE Trans. Ind. Inform., vol. 16, no. 5, pp. 3006–3017, May 2020. doi: 10.1109/TII.2019.2908958
    [4]
    L. Meng, Y. Hulovatyy, A. Striegel, and T. Milenković, “On the interplay between individuals’ evolving interaction patterns and traits in dynamic multiplex social networks,” IEEE Trans. Netw. Sci. Eng., vol. 3, no. 1, pp. 32–43, Jan–Mar. 2016. doi: 10.1109/TNSE.2016.2523798
    [5]
    X. Luo, Y. N. Xia, Q. S. Zhu, and Y. Li, “Boosting the K-Nearest-Neighborhood based incremental collaborative filtering,” Knowl.-Based Syst., vol. 53, pp. 90–99, Nov. 2013. doi: 10.1016/j.knosys.2013.08.016
    [6]
    R. Ahlswede, N. Cai, S. Y. R. Li, and R. W. Yeung, “Network information flow,” IEEE Trans. Inform. Theory, vol. 46, no. 4, pp. 1204–1216, Jul. 2000. doi: 10.1109/18.850663
    [7]
    N. Shahriar, R. Ahmed, S. R. Chowdhury, M. M. A. Khan, R. Boutaba, J. Mitra, and F. Zeng, “Virtual network embedding with guaranteed connectivity under multiple substrate link failures,” IEEE Trans. Commun., vol. 68, no. 2, pp. 1025–1043, Feb. 2020. doi: 10.1109/TCOMM.2019.2954410
    [8]
    T. X. Ji, C. Q. Luo, Y. F. Guo, Q. L. Wang, L. X. Yu, and P. Li, “Community detection in online social networks: A differentially private and parsimonious approach,” IEEE Trans. Comput. Soc. Syst., vol. 7, no. 1, pp. 151–163, Feb. 2020. doi: 10.1109/TCSS.2019.2957795
    [9]
    X. Luo, M. S. Shang, and S. Li, “Efficient extraction of non-negative latent factors from high-dimensional and sparse matrices in industrial applications,” in Proc. 16th Int. Conf. Data Mining, Barcelona, Spain, 2016, pp. 311–319.
    [10]
    M. Shirazi and A. Vosoughi, “On distributed estimation in hierarchical power constrained wireless sensor networks,” IEEE Trans. Signal Inform. Process. Netw., vol. 6, pp. 442–459, May 2020.
    [11]
    C. X. Li, G. Li, and P. K. Varshney, “Distributed detection of sparse stochastic signals with 1-bit data in tree-structured sensor networks,” IEEE Trans. Signal Process., vol. 68, pp. 2963–2976, Apr. 2020. doi: 10.1109/TSP.2020.2988598
    [12]
    T. Zhou, L. Y. Lv, and Y. C. Zhang, “Predicting missing links via local information,” Eur. Phys. J. B, vol. 71, no. 4, pp. 623–630, Oct. 2009. doi: 10.1140/epjb/e2009-00335-8
    [13]
    L. G. Liu, L. M. Wang, W. Wu, H. L. Jia, and Y. Zhang, “A novel hybrid-jump-based sampling method for complex social networks,” IEEE Trans. Comput. Soc. Syst., vol. 6, no. 2, pp. 241–249, Apr. 2019. doi: 10.1109/TCSS.2019.2893889
    [14]
    Z. H. You, M. C. Zhou, X. Luo, and S. Li, “Highly efficient framework for predicting interactions between proteins,” IEEE Trans. Cybern., vol. 47, no. 3, pp. 731–743, Mar. 2017. doi: 10.1109/TCYB.2016.2524994
    [15]
    B. Perozzi, R. Al-Rfou, and S. Skiena, “DeepWalk: Online learning of social representations,” in Proc. 20th ACM SIGKDD Int. Conf. Knowledge Discovery and Data Mining, New York, USA, 2014, pp. 701–710.
    [16]
    L. Bai, L. X. Cui, Z. H. Zhang, L. X. Xu, Y. Wang, and E. R. Hancock, “Entropic dynamic time warping kernels for co-evolving financial time series analysis,” IEEE Trans. Neural Netw. Learn. Syst., 2020, DOI: 10.1109/TNNLS.2020.3006738.
    [17]
    M. Seifikar, S. Farzi, and M. Barati, “C-Blondel: An efficient Louvain-based dynamic community detection algorithm,” IEEE Trans. Comput. Soc. Syst., vol. 7, no. 2, pp. 308–318, Apr. 2020. doi: 10.1109/TCSS.2020.2964197
    [18]
    A. Dal Col, P. Valdivia, F. Petronetto, F. Dias, C. T. Silva, and L. G. Nonato, “Wavelet-based visual analysis of dynamic networks,” IEEE Trans. Vis. Comput. Graph., vol. 24, no. 8, pp. 2456–2469, Aug. 2018. doi: 10.1109/TVCG.2017.2746080
    [19]
    A. Zhiyuli, X. Liang, Y. F. Chen, and X. Y. Du, “Modeling large-scale dynamic social networks via node embeddings,” IEEE Trans. Knowl. Data Eng., vol. 31, no. 10, pp. 1994–2007, Oct. 2019. doi: 10.1109/TKDE.2018.2872602
    [20]
    H. Wu, X. Luo, and M. C. Zhou, “Advancing non-negative latent factorization of tensors with diversified regularizations,” IEEE Trans. Serv. Comput., 2020, DOI: 10.1109/TSC.2020.2988760.
    [21]
    C. Y. Lu, J. S. Feng, Y. D. Chen, W. Liu, Z. C. Lin, and S. C. Yan, “Tensor robust principal component analysis with a new tensor nuclear norm,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 42, no. 4, pp. 925–938, Apr. 2020. doi: 10.1109/TPAMI.2019.2891760
    [22]
    V. W. Zheng, B. Cao, Y. Zheng, X. Xie, and Q. Yang, “Collaborative filtering meets mobile recommendation: A user-centered approach,” in Proc. 24th AAAI Conf. Artificial Intelligence, Atlanta, USA, 2010, pp. 236–241.
    [23]
    E. Acar, D. M. Dunlavy, T. G. Kolda, and M. Morup, “Scalable tensor factorizations with missing data,” in Proc. SIAM Int. Conf. Data Mining, Columbus, USA, 2010, pp. 701–712.
    [24]
    X. Luo, H. Wu, H. Q. Yuan, and M. C. Zhou, “Temporal pattern-aware QoS prediction via biased non-negative latent factorization of tensors,” IEEE Trans. Cybern., vol. 50, no. 5, pp. 1798–1809, May 2020. doi: 10.1109/TCYB.2019.2903736
    [25]
    Y. K. Wu, H. C. Tan, Y. Li, J. Zhang, and X. X. Chen, “A fused CP factorization method for incomplete tensors,” IEEE Trans. Neural Netw. Learn. Syst., vol. 30, no. 3, pp. 751–764, Mar. 2019. doi: 10.1109/TNNLS.2018.2851612
    [26]
    W. C. Zhang, H. L. Sun, X. D. Liu, and X. H. Guo, “Temporal QoS-aware web service recommendation via non-negative tensor factorization,” in Proc. 23rd Int. Conf. World Wide Web, Seoul, Korea (South), 2014, pp. 585–596.
    [27]
    T. Maehara, K. Hayashi, and K. I. Kawarabayashi, “Expected tensor decomposition with stochastic gradient descent,” in Proc. 30th AAAI Conf. Artificial Intelligence, Phoenix, USA, 2016, pp. 1919–1925.
    [28]
    X. Luo, H. J. Liu, G. P. Gou, Y. N. Xia, and Q. S. Zhu, “A parallel matrix factorization based recommender by alternating stochastic gradient decent,” Eng. Appl. Artif. Intell., vol. 25, no. 7, pp. 1403–1412, Oct. 2012. doi: 10.1016/j.engappai.2011.10.011
    [29]
    Y. W. Ji, Q. Wang, X. Li, and J. Liu, “A survey on tensor techniques and applications in machine learning,” IEEE Access, vol. 7, pp. 162950–162990, Oct. 2019. doi: 10.1109/ACCESS.2019.2949814
    [30]
    X. Luo, D. X. Wang, M. C. Zhou, and H. Q. Yuan, “Latent factor-based recommenders relying on extended stochastic gradient descent algorithms,” IEEE Trans. Syst.,Man,Cybern.:Syst., vol. 51, no. 2, pp. 916–926, Feb. 2021. doi: 10.1109/TSMC.2018.2884191
    [31]
    T. G. Kolda and B. W. Bader, “Tensor decompositions and applications,” SIAM Rev., vol. 51, no. 3, pp. 455–500, Aug. 2009. doi: 10.1137/07070111X
    [32]
    X. Luo, Z. D. Wang, and M. S. Shang, “An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data,” IEEE Trans. Syst.,Man,Cybern.:Syst., vol. 51, no. 6, pp. 3522–3532, Jun. 2021. doi: 10.1109/TSMC.2019.2930525
    [33]
    Z. L. Long, Z. T. Jiang, C. Wang, Y. Jin, Z. H. Cao, and Y. M. Li, “A novel approach to control of piezo-transducer in microelectronics packaging: PSO-PID and editing trajectory optimization,” IEEE Trans. Comp.,Packag. Manufact. Technol., vol. 10, no. 5, pp. 795–805, May 2020. doi: 10.1109/TCPMT.2020.2984701
    [34]
    H. Q. Wang, Y. Luo, W. P. An, Q. Y. Sun, J. Xu, and L. Zhang, “PID controller-based stochastic optimization acceleration for deep neural networks,” IEEE Trans. Neural Netw. Learn. Syst., vol. 31, no. 12, pp. 5079–5091, Dec. 2020. doi: 10.1109/TNNLS.2019.2963066
    [35]
    J. L. Li, Y. Yuan, T. Ruan, J. Chen, and X. Luo, “A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model,” Neurocomputing, vol. 427, pp. 29–39, Feb. 2021. doi: 10.1016/j.neucom.2020.11.029
    [36]
    Y. Z. Wang, Z. D. Wang, L. Zou, and H. L. Dong, “Multiloop decentralized H fuzzy PID-like control for discrete time-delayed fuzzy systems under dynamical event-triggered schemes,” IEEE Trans. Cybern., 2020, DOI: 10.1109/TCYB.2020.3025251.
    [37]
    S. Chopade, S. W. Khubalkar, A. S. Junghare, M. V. Aware, and S. Das, “Design and implementation of digital fractional order PID controller using optimal pole-zero approximation method for magnetic levitation system,” IEEE/CAA J. Autom. Sinica, vol. 5, no. 5, pp. 977–989, Sep. 2018. doi: 10.1109/JAS.2016.7510181
    [38]
    P. Borja, R. Ortega, and J. M. A. Scherpen, “New results on stabilization of port-hamiltonian systems via PID passivity-based control,” IEEE Trans. Autom. Control, vol. 66, no. 2, pp. 625–636, Feb. 2021. doi: 10.1109/TAC.2020.2986731
    [39]
    A. Behera, T. K. Panigrahi, P. K. Ray, and A. K. Sahoo, “A novel cascaded PID controller for automatic generation control analysis with renewable sources,” IEEE/CAA J. Autom. Sinica, vol. 6, no. 6, pp. 1438–1451, Nov. 2019. doi: 10.1109/JAS.2019.1911666
    [40]
    S. K. Pradhan and B. Subudhi, “Position control of a flexible manipulator using a new nonlinear self-tuning PID controller,” IEEE/CAA J. Autom. Sinica, vol. 7, no. 1, pp. 136–149, Jan. 2020.
    [41]
    X. Luo, Z. G. Liu, S. Li, M. S. Shang, and Z. D. Wang, “A fast non-negative latent factor model based on generalized momentum method,” IEEE Trans. Syst.,Man,Cybern.:Syst., vol. 51, no. 1, pp. 610–620, Jan. 2021. doi: 10.1109/TSMC.2018.2875452
    [42]
    G. Takács, I. Pilászy, B. Németh, and D. Tikk, “Scalable collaborative filtering approaches for large recommender systems,” J. Mach. Learn. Res., vol. 10, pp. 623–656, Dec. 2009.
    [43]
    D. Wu, X. Luo, M. S. Shang, Y. He, G. Y. Wang, and M. C. Zhou, “A deep latent factor model for high-dimensional and sparse matrices in recommender systems,” IEEE Trans. Syst.,Man,Cybern.:Syst., vol. 51, no. 7, pp. 4285–4296, Jul. 2021. doi: 10.1109/TSMC.2019.2931393
    [44]
    Y. Koren, “Collaborative filtering with temporal dynamics,” in Proc. 15th ACM SIGKDD Int. Conf. Knowledge Discovery and Data Mining, Paris, France, 2009, pp. 447–456.
    [45]
    E. E. Papalexakis, C. Faloutsos, and N. D. Sidiropoulos, “Tensors for data mining and data fusion: Models, applications, and scalable algorithms,” ACM Trans. Intell. Syst. Technol., vol. 8, no. 2, Article No. 16, Jan. 2017.
    [46]
    L. Duan, S. Ma, C. Aggarwal, T. J. Ma, and J. P. Huai, “An ensemble approach to link prediction,” IEEE Trans. Knowl. Data Eng., vol. 29, no. 11, pp. 2402–2416, Nov. 2017. doi: 10.1109/TKDE.2017.2730207
    [47]
    C. H. Nguyen and H. Mamitsuka, “Latent feature kernels for link prediction on sparse graphs,” IEEE Trans. Neural Netw. Learn. Syst., vol. 23, no. 11, pp. 1793–1804, Nov. 2012. doi: 10.1109/TNNLS.2012.2215337
    [48]
    L. H. Zhu, D. Guo, J. M. Yin, G. Ver Steeg, and A. Galstyan, “Scalable temporal latent space inference for link prediction in dynamic social networks,” IEEE Trans. Knowl. Data Eng., vol. 28, no. 10, pp. 2765–2777, Oct. 2016. doi: 10.1109/TKDE.2016.2591009
    [49]
    J. H. Ye, H. Cheng, Z. Zhu, and M. H. Chen, “Predicting positive and negative links in signed social networks by transfer learning,” in Proc. 22rd Int. Conf. World Wide Web, Rio de Janeiro, Brazil, 2013, pp. 1477–1488.
    [50]
    Z. Q. Wang, J. Y. Liang, and R. Li, “A fusion probability matrix factorization framework for link prediction,” Knowl.-Based Syst., vol. 159, pp. 72–85, Nov. 2018. doi: 10.1016/j.knosys.2018.06.005
    [51]
    H. Wang, X. J. Shi, and D. Y. Yeung, “Relational deep learning: A deep latent variable model for link prediction,” in Proc. 31st AAAI Conf. Artificial Intelligence, San Francisco, USA, 2017, pp. 2688–2694.
    [52]
    J. Y. Chen, J. Zhang, X. H. Xu, C. B. Fu, D. Zhang, Q. P. Zhang, and Q. Xuan, “E-LSTM-D: A deep learning framework for dynamic network link prediction,” IEEE Trans. Syst.,Man,Cybern.:Syst., vol. 51, no. 6, pp. 3699–3712, Jun. 2021. doi: 10.1109/TSMC.2019.2932913
    [53]
    X. Y. Li, N. Du, H. Li, K. Li, J. Gao, and A. D. Zhang, “A deep learning approach to link prediction in dynamic networks,” in Proc. SIAM Int. Conf. Data Mining, Philadelphia, USA, 2014, pp. 289–297.
    [54]
    A. Ozcan and S. G. Oguducu, “Link prediction in evolving heterogeneous networks using the NARX neural networks,” Knowl. Inform. Syst., vol. 55, no. 2, pp. 333–360, Jul. 2017.
    [55]
    T. S. Li, B. Wang, Y. S. Jiang, Y. Zhang, and Y. H. Yan, “Restricted Boltzmann machine-based approaches for link prediction in dynamic networks,” IEEE Access, vol. 6, pp. 29940–29951, May 2018. doi: 10.1109/ACCESS.2018.2840054
    [56]
    A. Grover and J. Leskovec, “Node2vec: Scalable feature learning for networks,” in Proc. 22nd ACM SIGKDD Int. Conf. Knowledge Discovery and Data Mining, San Francisco, USA, 2016, pp. 855–864.
    [57]
    C. B. Fu, M. H. Zhao, L. Fan, X. Y. Chen, J. Y. Chen, Z. F. Wu, Y. X. Xia, and Q. Xuan, “Link weight prediction using supervised learning methods and its application to yelp layered network,” IEEE Trans. Knowl. Data Eng., vol. 30, no. 8, pp. 1507–1518, Aug. 2018. doi: 10.1109/TKDE.2018.2801854
    [58]
    Z. Q. Wang, J. Y. Liang, R. Li, and Y. H. Qian, “An approach to cold-start link prediction: Establishing connections between non-topological and topological information,” IEEE Trans. Knowl. Data Eng., vol. 28, no. 11, pp. 2857–2870, Nov. 2016. doi: 10.1109/TKDE.2016.2597823
    [59]
    F. Xia, Z. Chen, W. Wang, J. Li, and L. T. Yang, “MVCWalker: Random walk-based most valuable collaborators recommendation exploiting academic factors,” IEEE Trans. Emerg. Top. Comput., vol. 2, no. 3, pp. 364–375, Sep. 2014. doi: 10.1109/TETC.2014.2356505
    [60]
    A. De, S. Bhattacharya, S. Sarkar, N. Ganguly, and S. Chakrabarti, “Discriminative link prediction using local, community, and global signals,” IEEE Trans. Knowl. Data Eng., vol. 28, no. 8, pp. 2057–2070, Aug. 2016. doi: 10.1109/TKDE.2016.2553665
    [61]
    W. B. Liu, Z. D. Wang, X. H. Liu, N. Y. Zeng, and D. Bell, “A novel particle swarm optimization approach for patient clustering from emergency departments,” IEEE Trans. Evol. Comput., vol. 23, no. 4, pp. 632–644, Aug. 2019. doi: 10.1109/TEVC.2018.2878536
    [62]
    L. Xin, Y. Yuan, M. C. Zhou, Z. G. Liu, and M. S. Shang, “Non-negative latent factor model based on β-divergence for recommender systems,” IEEE Trans. Syst.,Man,Cybern.:Syst., vol. 51, no. 8, pp. 4612–4623, Aug. 2021. doi: 10.1109/TSMC.2019.2931468
    [63]
    A. H. Khan, Z. L. Shao, S. Li, Q. X. Wang, and N. Guan, “Which is the best PID variant for pneumatic soft robots? An experimental study” IEEE/CAA J. Autom. Sinica, vol. 7, no. 2, pp. 451–460, Mar. 2020. doi: 10.1109/JAS.2020.1003045
    [64]
    X. Luo, W. Qin, A. N. Dong, K. Sedraoui, and M. C. 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
    [65]
    B. Liu, K. M. Huang, J. Q. Li, and M. C. Zhou, “An incremental and distributed inference method for large-scale ontologies based on MapReduce paradigm,” IEEE Trans. Cybern., vol. 45, no. 1, pp. 53–64, Jan. 2015. doi: 10.1109/TCYB.2014.2318898
    [66]
    S. G. Deng, L. T. Huang, J. Taheri, J. W. Yin, M. C. Zhou, and A. Y. Zomaya, “Mobility-aware service composition in mobile communities,” IEEE Trans. Systems,Man,and Cybernetics:Systems, vol. 47, no. 3, pp. 555–568, Mar. 2017.
    [67]
    H. Han, M. C. Zhou, X. U. Shang, W. Cao and A. Abusorrah, “KISS+ for rapid and accurate pedestrian re-identification,” IEEE Trans Intelligent Transportation Systems, vol. 22, no. 1, pp. 394–403, Jan. 2021.
    [68]
    A. De, S. Bhattacharya, S. Sarkar, N. Ganguly, and S. Chakrabarti, “Discriminative link prediction using local, community, and global signals,” IEEE Trans. Knowl. Data Eng, vol. 21, no. 2, pp. 1314–1345, Aug. 2019.
    [69]
    G. S. Wei, Q. W. Wu and M. C. Zhou, “A hybrid probabilistic multiobjective evolutionary algorithm for commercial recommendation systems,” IEEE Trans. Computational Social Systems, vol. 8, no. 3, pp. 589–598, Jun. 2021.

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(11)  / Tables(3)

    Article Metrics

    Article views (978) PDF downloads(79) Cited by()

    Highlights

    • It proposes a PLFT model that performs latent feature analysis on an HDI tensor with high efficiency and accuracy
    • It presents detailed algorithm design and analysis for PLFT, which provides specific guidance for researchers to implement a PLFT model for DWDN analyses
    • It conducts empirical studies on two large-scale DWDNs from a real system to show PLFT’s impressively high efficiency and competitive link prediction accuracy

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return