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01794nam a2200217Ia 4500 |
| 001 |
10.3389-fphy.2021.746660 |
| 008 |
220427s2021 CNT 000 0 und d |
| 020 |
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|a 2296424X (ISSN)
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| 245 |
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|a Analyzing Levels of Concern About Joint Punishment for Dishonesty Using the Visibility Graph Network
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| 260 |
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|b Frontiers Media S.A.
|c 2021
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| 856 |
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|z View Fulltext in Publisher
|u https://doi.org/10.3389/fphy.2021.746660
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|a Joint punishment for dishonesty is an important means of administrative regulation. This research analyzed the dynamic characteristics of time series data from the Baidu search index using the keywords “joint punishment for dishonesty” based on a visibility graph network. Applying a visibility graph algorithm, time series data from the Baidu Index was transformed into complex networks, with parameters calculated to analyze the topological structure. Results showed differences in the use of joint punishment for dishonesty in certain provinces by calculating the parameters of the time series network from January 1, 2020 to May 27, 2021; it was also shown that most of the networks were scale-free. Finally, the results of K-means clustering showed that the 31 provinces (excluding Hong Kong, Macao and Taiwan) can be divided into four types. Meanwhile, by analyzing the national Baidu Index data from 2020 to May 2021, the period of the time series data and the influence range of the central node were found. © Copyright © 2021 Qu, Zhang and Li.
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|a Baidu index
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|a joint punishment for dishonesty
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|a social credit system
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| 650 |
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|a time series network analysis
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| 650 |
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|a visibility network
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| 700 |
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|a Li, F.
|e author
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|a Qu, Z.
|e author
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|a Zhang, Y.
|e author
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| 773 |
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|t Frontiers in Physics
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