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International Conference of Pioneering Computer Scientists, Engineers and Educators

ICPCSEE 2019: Data Science pp 62–75Cite as

Community Evolution Based on Tensor Decomposition

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Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1058))

Abstract

With the high dimensionality of data, the method of tensor decomposition has attracted much attention in the field of data research and analysis. The tensor decomposition is well reflected in the study of high-dimensional data. The existing research uses the results of tensor decomposition to conduct community discovery. Based on the existing research, this paper presents a method to study community evolution using the results of tensor decomposition. The feature matrix obtained by the tensor decomposition algorithm was analyzed, and the real-time activity of the community with the feature matrix with time slice direction was studied to obtain the event process of community evolution. Experimental results in real data sets show that this method can well analyze dynamic events in the dataset and community evolution events.

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Author information

Authors and Affiliations

  1. School of Electronic and Information Engineering, Lanzhou Jiaotong University, Lanzhou, 730070, China

    Yuxuan Liu, Guanghui Yan, Jianyun Ye & Zongren Li

Authors
  1. Yuxuan Liu
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  2. Guanghui Yan
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  3. Jianyun Ye
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  4. Zongren Li
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Corresponding author

Correspondence to Yuxuan Liu .

Editor information

Editors and Affiliations

  1. Guilin University of Technology, Guilin, China

    Xiaohui Cheng

  2. Northeast Forestry University, Harbin, China

    Weipeng Jing

  3. Harbin University of Science and Technology, Harbin, China

    Xianhua Song

  4. National Academy of Guo Ding Institute of Data Science, Harbin, China

    Zeguang Lu

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© 2019 Springer Nature Singapore Pte Ltd.

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Liu, Y., Yan, G., Ye, J., Li, Z. (2019). Community Evolution Based on Tensor Decomposition. In: Cheng, X., Jing, W., Song, X., Lu, Z. (eds) Data Science. ICPCSEE 2019. Communications in Computer and Information Science, vol 1058. Springer, Singapore. https://doi.org/10.1007/978-981-15-0118-0_6

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  • DOI: https://doi.org/10.1007/978-981-15-0118-0_6

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-15-0117-3

  • Online ISBN: 978-981-15-0118-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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