ESNMF: Evolutionary Symmetric Nonnegative Matrix Factorization for Dissecting Dynamic Microbial Networks

  • Yuanyuan Ma
  • Xiaohua Hu
  • Tingting He
  • Xianchao Zhu
  • Meijun Zhou
  • Xingpeng JiangEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10956)


Dynamic network is drawing more and more attention due to its potential in capturing time-dependent phenomena such as online public opinion and biological system. Microbial interaction networks that model the microbial system are often dynamic, static analysis methods are difficult to obtain reliable knowledge on evolving communities. To fulfill this gap, a dynamic clustering approach based on evolutionary symmetric nonnegative matrix factorization (ESNMF) is used to analyze the microbiome time-series data. To our knowledge, this is the first attempt to extract dynamic modules across time-series microbial interaction network. ESNMF systematically integrates temporal smoothness cost into the objective function by simultaneously refining the clustering structure in the current network and minimizing the clustering deviation in successive timestamps. We apply the proposed framework on a human microbiome datasets from infants delivered vaginally and ones born via C-section. The proposed method cannot only identify the evolving modules related to certain functions of microbial communities, but also discriminate differences in two kinds of networks obtained from infants delivered vaginally and via C-section.


Dynamic network Evolutionary module Symmetric nonnegative matrix factorization Microbiome 



This research is supported by the National Natural Science Foundation of China (No. 61532008), the Excellent Doctoral Breeding Project of CCNU, the Self-determined Research Funds of CCNU from the Colleges’ Basic Research and Operation of MOE (No. CCNU16KFY04).


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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Yuanyuan Ma
    • 1
  • Xiaohua Hu
    • 2
  • Tingting He
    • 2
  • Xianchao Zhu
    • 2
  • Meijun Zhou
    • 2
  • Xingpeng Jiang
    • 2
    Email author
  1. 1.School of Information ManagementCentral China Normal UniversityWuhanChina
  2. 2.School of ComputerCentral China Normal UniversityWuhanChina

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