社交网络信息传播预测与特定信息抑制

曹玖新; 高庆清; 夏蓉清; 刘伟佳; 朱雪林; 刘波

doi:10.7544/issn1000-1239.2021.20200809

社交网络信息传播预测与特定信息抑制

¹(东南大学网络空间安全学院南京 211189)
²(东南大学计算机科学与工程学院南京 211189) (jx.cao@seu.edu.cn)

基金项目: 国家自然科学基金项目(61772133, 61972087)；国家社会科学基金项目(19@ZH014)；江苏省重点研发计划项目(BE2018706)；江苏省自然科学基金项目(SBK2019022870) ；江苏省计算机网络技术重点实验室；江苏省网络与信息安全重点实验室(BM2003201)；计算机网络和信息集成教育部重点实验室(93K-9)

详细信息

中图分类号: TP391
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出版历程
- 发布日期: 2021-06-30

Information Propagation Prediction and Specific Information Suppression in Social Networks

¹(School of Cyber Science and Engineering, Southeast University, Nanjing 211189)
²(School of Computer Science and Engineering, Southeast University, Nanjing 211189)

Funds: This work was supported by the National Natural Science Foundation of China (61772133, 61972087), the National Social Science Foundation of China (19@ZH014), the Jiangsu Key Research and Development Program (BE2018706), the Natural Science Foundation of Jiangsu Province (SBK2019022870), the Jiangsu Key Laboratory of Computer Networking Technology, the Jiangsu Provincial Key Laboratory of Network and Information Security (BM2003201), and the Key Laboratory of Computer Network and Information Integration of Ministry of Education of China (93K-9).

摘要

摘要: 近年来，随着Twitter、Facebook、新浪微博等社交网站用户数量的激增，信息数量急剧膨胀，隐藏在海量信息中的不实信息的传播带来了不良的影响，如何调控或抑制特定信息的传播是网络信息管理面临的一项技术挑战.为了解决这一问题，首先从真实微博网络出发，基于机器学习方法提出了不依赖于传播模型的独立信息转发预测机制，从而对信息的传播进行预测；其次，基于独立级联模型，综合考虑本文场景的特殊性，提出了异步信息不平等竞争传播模型作为特定信息与免疫信息的竞争传播机制；最后，提出了3个种子节点集合选择算法，通过向选择的种子节点注入免疫信息使得免疫信息在网络中广泛传播从而抑制特定信息的传播.基于真实社交网站数据的实验证明，提出的信息传播预测模型以及种子节点选取算法对特定信息传播的调控和抑制具有良好的效果.
- 社交网络 /
- 竞争传播 /
- 传播模型 /
- 转发预测 /
- 信息抑制
Abstract: In recent years, with the increasing number of users in social networks such as Twitter, Facebook and Sina Weibo, the amount of information has rapidly expanded. The spread of untrue information hidden in massive information has brought adverse effects. How to regulate or suppress the spread of specific information is a technical challenge faced by network information management. In order to solve this problem, first of all, the independent information forwarding prediction mechanism based on machine learning method, which does not depend on the propagation model is proposed, so as to predict the information propagation. Secondly, based on the independent cascade model, considering the particularity of the scenario in this paper, the asynchronous information unequal competition propagation model is proposed as the competitive propagation mechanism of specific information and immune information. Finally, three selection algorithms of seed nodes are proposed and the immune information is widely spread in the network by injecting immune information into the seed nodes, so as to suppress the spread of specific information. Experiments based on real social network data show that the information propagation prediction model and the selection algorithms of seed nodes proposed have good effects on the regulation and suppression of specific information propagation.
- social network /
- competition propagation /
- propagation model /
- forwarding prediction /
- information suppression

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