基于多主题线性阈值的社交网络舆情传播控制方法
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作者单位:

浙江工业大学

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中图分类号:

TP315

基金项目:

国家自然科学基金项目(71173194, 71571162, 71772164);国家社科基金应急管理体系建设研究专项项目(20VYJ073);国家社科基金重大项目(17ZDA088);浙江省自然基金一般项目(LY18D010006)。


Method Used to Control the Spread of Social Network Public Opinion on the Basis of Multi-topic Linear Threshold Model
Author:
Affiliation:

Zhejiang University of Technology

Fund Project:

National Natural Science Foundation of China (71173194, 71571162, 71772164); National Social Science Fund Emergency Management System construction special project (20VYJ073); National Social Science Fund Major Project (17ZDA088); Zhejiang Natural Fund General Project (LY18D010006).

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    摘要:

    随着近年来信息技术的不断发展,社交网络已然成为用户相互沟通、发表自身观点的重要场所。社交网络信息具有传播速度快、影响力强等特征,且不同主题舆情信息间还可能存在共振或互斥,故对社会稳定与网络安全造成负面影响。基于此,本研究提出了一种考虑多主题和预算约束的MTBC舆情传播控制问题。首先,找到一组节点,使得在成本较小的情况下,对负面舆情信息进行有效控制和阻塞。其次,提出了一种近似比为1-√e的IGA近似贪婪算法,并针对大型社交网络数据集提出了一种IGEA扩展算法,利用树状数据结构实现快速更新与目标函数计算。最后,利用真实社交网络数据集实进行了实验,结果表明:本文所提算法较现有的舆情传播控制算法具有更高的效率与精确度,且IGEA算法适用于大规模社交网络数据集。

    Abstract:

    With the continuous development of information technology in recent years, social network has become the main platform for its users to communicate ad express their own opinions. Characterized by rapid spread speed and great influence, even there may be resonance or mutual exclusion between public opinion information on different topics, social network information plays a negative influence on social stability and network security. In accordance with this situation, this study proposes a problem about the spread and control of MTBC public opinion based on multiple topics and budget constraints. Firstly, a set of nodes was found so that negative public opinion information can be effectively controlled and prevented at low cost. Secondly, an IGA approximate greedy algorithm with an approximate ratio of 1-√e was proposed, and an IGEA extended algorithm was raised for large social network data set. Rapid update and objective function calculation were achieved by using tree structured database. Finally, a numerical example was realized by employing real social network data set. The result showed that the algorithm proposed in this paper has higher efficiency and accuracy than the existing algorithm used to control the spread of public opinion information. And IGEA algorithm is suitable for large-scale social network data sets.

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  • 收稿日期:2020-11-03
  • 最后修改日期:2021-02-10
  • 录用日期:2021-03-03
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