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Small Data: Effective Data Based on Big Communication Research in Social Networks

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Abstract

Big data research is difficult because of its complex structure, vast data storage, and unpredictable change. Social network communication involves a significant amount of incomputable data created by wireless devices across the world. Such data can be used to analyze human activities, seek certain patterns using communication data, and predict emergencies. However, most data are effect of human to research our activities. So, recording effective node distribution and investigating the topological structure in communication are particularly important in big data communication. This study establishes a big data communication simulation environment by searching small data and calculating the influence of small data nodes. The experiment shows that 1% of small data can connect 75% of communication nodes and 20% of small data can transmit 80% of data packets.

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Acknowledgements

Foundation Items: This work was supported in part by Major Program of National Natural Science Foundation of China (71633006); The National Natural Science Foundation of China (61672540, 61379057); China Postdoctoral Science Foundation funded project (2017M612586); The Postdoctoral Science Foundation of Central South University (185684).

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Authors and Affiliations

  1. School of Information science and engineering, Central South University, Changsha, China

    Jia Wu

  2. School of Software, Central South University, Changsha, China

    Jia Wu, Ming Zhao & Zhigang Chen

Authors
  1. Jia Wu
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  2. Ming Zhao
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  3. Zhigang Chen
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Correspondence to Jia Wu.

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Wu, J., Zhao, M. & Chen, Z. Small Data: Effective Data Based on Big Communication Research in Social Networks. Wireless Pers Commun 99, 1391–1404 (2018). https://doi.org/10.1007/s11277-017-5191-2

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  • DOI: https://doi.org/10.1007/s11277-017-5191-2

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