Hybrid Model Based Sampling Algorithm to Infer Dynamic Complex Network
Volume 13, Number 2, March 2017 - Paper 12 - pp. 231-239
JIN GUO1,2, SHENGBING ZHANG1 AND ZHENG QIU3
1School of Computing, Northwestern Polytechnical University, Xi’an 710072, Shaanxi, China
2School of Electronics and Information Engineering, Xi'an Technological University, Xi’an 710032, Shaanxi, China
3Aeronautical Computing Technique Research Institute, 15th Lab, Xi'an 710000, Shaanxi, China
(Received on September 03, 2016, revised on October 16, 2016)
Inferring dynamic complex network through a small set of samples is a challenging problem in the field of biological network, social network and transportation network, which can help improve understanding of complex network systems. In this letter, a new Hybrid Model based Latent Variables Sampling algorithm is presented to address the problems of high computation complexity and low accuracy faced by traditional approaches. Experimental results on simulated and real data sets show that the presented method possesses better reasoning performance and significantly improves the precision and efficiency of network inference especially when compared with the other three approaches. Under different dimensions, HM-LVS still has higher accuracy (average 80%) and can effectively reverse engineering dynamic complex networks from time series data.
. Lu T, Liang H, Li H, et al. High-dimensional odes coupled with mixed-effects modeling techniques for dynamic gene regulatory network identification. Journal of the American Statistical Association, 2011; 106: 1242-1258.
. Hanshan Li. Research on target information optics communications transmission characteristic and performance in multi-screens testing system. Optics communications. 2016; 364: 139-144.
. Zidong W. An extended kalman filtering approach to modeling nonlinear dynamic gene regulatory networks via short gene expression time series, IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2009; 6(3): 410-419.
. Nguyen X V, Chetty M, Coppel R, et al,Learning globally optimal dynamic bayesian network with the mutual information test criterion, Bioinformatics, 2011; 27(19): 2765-2766.
. Lebre S, Becq J, Devaux F, et al. Statistical inference of the time-varying structure of gene-regulation networks, BMC Systems Biology, 2010; 4(1), 130.
. Wu, B., Feng, Y., Zheng, H. Posterior Belief Clustering Algorithm for Energy-efficient Tracking in Wireless Sensor Networks, International Journal on Smart Sensing and Intelligent Systems, 2014; 7(3) : 925-941.
. Kawaguchi J, Ninomiya T, Miyazawa Y. Stochastic approach to robust flight control design using hierarchy-structured dynamic inversion, Journal of Guidance, Control, and Dynamics, 2011; 34(5) :1573-1577.
. Li Hanshan. Limited Magnitude Calculation Method and Optics Detection Performance in a Photoelectric Tracking System, Applied Optics, 2015; 54(7), pp.1612-1617.
. Zhang, J., Yu, J., Chi, N. Multi-Modulus Blind Equalizations for Coherent Quadrature Duobinary Spectrum Shaped PM-QPSK Digital Signal Processing, Journal of Lightwave Technology, 2013; 31(7) : 1073-1078.
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