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融入度相关性与社区识别的社交网络舆情信源发现方法

吴功兴1,3,琚春华1,3,杨之骄2   

  1. 1. 浙江工商大学管理工程与电子商务学院, 杭州 310018; 2. 宁波诺丁汉大学商学院, 宁波 315175;3. 浙江工商大学统计与数学学院 , 杭州 310018
  • 出版日期:2021-09-25 发布日期:2021-11-25

吴功兴, 琚春华, 杨之骄. 融入度相关性与社区识别的社交网络舆情信源发现方法[J]. 系统科学与数学, 2021, 41(9): 2492-2504.

WU Gongxing, JU Chunhua, YANG Zhijiao. The Method Used to Discover the Information Source of Social Network Public Opinion Integrated with Degree Correlations and Community Identification[J]. Journal of Systems Science and Mathematical Sciences, 2021, 41(9): 2492-2504.

The Method Used to Discover the Information Source of Social Network Public Opinion Integrated with Degree Correlations and Community Identification

WU Gongxing1,3 ,JU Chunhua1,3 ,YANG Zhijiao2   

  1. 1. School of Management Science & Engineering, Zhejiang Gongshang University, Hangzhou 310018; 2. School of Business, University of Nottingham Ningbo, Ningbo 315175; 3. Institute of Statistics, Zhejiang Gongshang University, Hangzhou 310018
  • Online:2021-09-25 Published:2021-11-25
近年来, 社交网络的不断发展提升了网络信息传播的速度, 故识别能 使舆情信息影响力最大化的最小节点集已成为信息科学的重要问题之一. 文章融入度相 关性与社区识别, 设计DCCI社交网络环境的舆情信源集发现方法, 并在网络中对所提方法进行验证. 由实验结果可知, 文章所提算法的精度略优于其他算法, 且运行效率较高.
In recent years, the continuous development of social networks has improved the speed of network information transmission, so the identification of the minimum node set that can maximize the influence of public opinion information has become one of the important issues in information science. This paper integrates degree correlation and community identification, designs a method to find public opinion information source set in the DCCI social network environment, and verifies the proposed method in the network. The experimental results show that the accuracy of the proposed algorithm is slightly better than other algorithms, and the running efficiency is higher.
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