• • 上一篇    

基于最优演化策略的社交网络舆情传播控制方法

冶治玲, 王志军   

  1. 宁波财经学院, 宁波 315175
  • 收稿日期:2021-09-07 修回日期:2022-02-05 发布日期:2022-07-29
  • 基金资助:
    教育部人文社会科学研究一般项目(19YJA880063),浙江省高校重大人文社科攻关计划青年重点项目(2018QN032)资助课题.

冶治玲, 王志军. 基于最优演化策略的社交网络舆情传播控制方法[J]. 系统科学与数学, 2022, 42(6): 1596-1615.

YE Zhiling, WANG Zhijun. Control Method of Social Network Public Opinion Spreading Based on Evolutionary Stability Strategy[J]. Journal of Systems Science and Mathematical Sciences, 2022, 42(6): 1596-1615.

Control Method of Social Network Public Opinion Spreading Based on Evolutionary Stability Strategy

YE Zhiling, WANG Zhijun   

  1. Ningbo University of Finance and Economics, Ningbo 315175
  • Received:2021-09-07 Revised:2022-02-05 Published:2022-07-29
文章提出了基于信任机制的舆情传播模型,引入个体预期收益与最优演化搜索动力学对群体搜索策略进行分析.针对信息搜索策略,分析非合作与合作环境中的演化稳定搜索策略,以最大化群体预期信息收益.在考虑信息搜索价值的情况下,分别讨论正面/负面舆情信息的演化与最优搜索策略的并行预期收益.最后,基于真实社交网络数据集对进行数值算例分析,结果表明:所提算法较经典舆情传播控制算法而言具有较高的精度与效率.
In this study, a public opinion propagation model based on trust mechanism is proposed, and individual expected return and optimal evolutionary search dynamics are introduced to analyze the group search strategy. The evolutionarily stable search strategies in non-cooperative and cooperative environments are analyzed to maximize the group's expected information returns. Considering the value of public opinion search, the evolution of positive and negative public opinion information and the parallel expected return of optimal search strategy are discussed respectively. Finally, a numerical example is given based on the real social network data set. The results show that the proposed algorithm has higher performance and efficiency than the classical public opinion propagation control algorithm.

MR(2010)主题分类: 

()
[1] 李丹丹,马静.双层社会网络上的舆情传播动力学分析.系统工程理论与实践, 2017, 37(10):2672-2679.(Li D D, Ma J.Public opinion spreading dynamics in a two-layer social network.Systems Engineering-Theory&Practice, 2017, 37(10):2672-2679.)
[2] 王卷乐,张敏,韩雪华,等.COVID-19疫情防控中的中国公众舆情时空演变特征.地理学报, 2020, 75(11):2490-2504.(Wang J L, Zhang M, Han X H, et al.Spatio-temporal evolution and regional differences of the public opinion on the prevention and control of COVID-19 epidemic in China.Acta Georaphica Sinica, 2020, 75(11):2490-2504.)
[3] 王志飞,史培腾,邓苏,等.基于节点社会特征的机会网络最优发送策略.通信学报,2016, 37(6):163-168.(Wang Z F, Shi P T, Deng S, et al.Optimal forwarding policy in opportunistic based on social features of nodes.Journal on Communications, 2016, 37(6):163-168.)
[4] Song C, Hsu W, Lee M L.Temporal influence blocking:Minimizing the effect of misinformation in social networks.2017 IEEE 33rd International Conference on Data Engineering, 2017, 847-858.
[5] 周楠,杜攀,靳小龙,等.面向舆情事件的子话题标签生成模型ET-TAG.计算机学报,2018, 41(7):1490-1503.(Zhou N, Du P, Jin X L, et al.ET-TAG:A tag generation model for the sub-topics of public opinion events.Chinese Journal of Computers, 2018, 41(7):1490-1503.)
[6] Zhu W, Yang W, Xuan S, et al.Location-aware influence blocking maximization in social networks.IEEE Access, 2018, 6:61462-61477.
[7] Zhu W, Yang W, Xuan S, et al.Location-based seeds selection for influence blocking maximization in social networks.IEEE Access, 2019, 7:27272-27287.
[8] Farajtabar M, Ye X, Harati S, et al.Multistage campaigning in social networks.Neurocomputing, 2016, 8(1):4718-4726.
[9] Ríos S A, Aguilera F, Nunez-Gonzalez J D, et al.Semantically enhanced network analysis for influencer identification in online social networks.Neurocomputing, 2017, 326(31):71-81.
[10] 郝亚洲,郑庆华,陈艳平,等.面向网络舆情数据的异常行为识别.计算机研究与发展, 2016, 53(3):611-620.(Hao Y Z, Zheng Q H, Chen Y P, et al.Recognition of abnormal behavior based on data of public opinion on the web.Journal of Computer Research and Development, 2016, 53(3):611-620.)
[11] 宋彪,朱建明,黄启发.基于群集动力学和演化博弈论的网络舆情疏导模型.系统工程理论与实践, 2014, 34(11):2984-2994.(Song B, Zhu J M, Huang Q F.The Internet public opinion grooming model based on cluster dynamics and evolutionary game theory.Systems Engineering-Theory&Practice, 2014, 34(11):2984-2994.)
[12] Zhao L, Yin J, Song Y.An exploration of rumor combating behavior on social media in the context of social crises.Computers in Human Behavior, 2016, 58(5):25-36.
[13] Xia L L, Jiang G P, Song B, et al.Rumor spreading model considering hesitating mechanism in complex social networks.Physica A:Statal Mechanics&Its Applications, 2015, 437(437):295-303.
[14] 严培胜,王先甲,张青.公共资产配置与预算管理的演化博弈分析.系统工程理论与实践, 2020, 40(11):2872-2884.(Yan P S, Wang X J, Zhang Q.Analysis on the efficient allocation of public assets and budget management based on evolution game theory.Systems Engineering-Theory&Practice, 2020, 40(11):2872-2884.)
[15] Chakraborti A, Toke I M, Patriarca M, et al.Econophysics review:I.Empirical facts.Quantitative Finance, 2011, 11(7):991-1012.
[16] Henkel C.From quantum mechanics to finance:Microfoundations for jumps, spikes and high volatility phases in diffusion price processes.Physica A:Statistical Mechanics and Its Applications, 2017, 469:447-458.
[17] 张其亮,俞祚明.基于种群的多层次迭代贪婪算法优化阻塞流水车间调度问题.计算机集成制造系统,2016, 22(10):2315-2322.(Zhang Q L, Yu Z M.Population-based multi-layer iterated gredyalgorithm for solving blocking flow shop scheduling problem.Computer Integrated Manufacturing Systems, 2016, 22(10):2315-2322.)
[18] José M, Stollenwerk N, Pinto A.Stationarity in moment closure and quasi-stationarity of the SIS model.Mathematical Biosciences, 2012, 236(2):126-131.
[19] Krause S M, Weyhausen-Brinkmann F, Bornholdt S.Repulsion in controversial debate drives public opinion into fifty-fifty stalemate.Physical Review E, 2019, 100(41):4-23.
[20] Zheng X, Luo Y, Sun L, et al.A new recommender system using context clustering based on matrix factorization techniques.Chinese Journal of Electronics, 2016, 25(2):334-340.
[21] Zhang Y, Prakash B A.Data-aware vaccine allocation over large networks.ACM Transactions on Knowledge Discovery from Data, 2015, 10(2):1-32.
[22] Ma J, Gao W, Mitra P, et al.Detecting rumors from microblogs with recurrent neural networks.Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence, International Joint Conference on Artificial Intelligence, 2016, 1(1):3818-3824.
[23] Paluch R, Lu X, Suchecki K, et al.Fast and accurate detection of spread source in large complex networks.Scientific Reports, 2018, 8(1):1-10.
[24] 米源,唐恒亮.基于图卷积网络的谣言鉴别研究.计算机工程与应用, 2021, 57(13):161-167.(Mi Y, Tang H L.Rumor identification research based on graph convolutional network.Computer Engineering and Applications, 2021, 57(13):161-167.)
[25] Bourigault S, Lamprier S, Galinari P.Representation learning for information diffusion through social networks:An embedded cascade model.Proc.of the 9th ACM Int Conf on Web Search and Data Mining, New York:ACM, 2016, 1(1):573-582.
[26] Chen W, Wang Y J, Yang S Y.Eficient influence maximization in social networks.Proc of the 15th ACM SIGKDD Int Conf on Knowledge Discovery and Data Mining, New York:ACM, 2009, 1(1):199-208.
[27] Liu C, Zhang Z K.Information spreading on dynamic social networks.Communications in Nonlinear Science and Numerical Simulation, 2014, 19(4):896-904.
[28] Zhu L, Wang Y G.Rumor spreading model with noise interference in complex social networks.Physica A:Statistical Mechanics and Its Applications, 2017, 469(1):750-760.
[29] Lu L, Chen D B, Zhou T.The small world yields the most effective information spreading.New Journal of Physics, 2011, 13(12):12-30.
[1] 彭娟娟, 田超. 社交网络环境下基于单值中智信息的大规模群决策方法[J]. 系统科学与数学, 2022, 42(4): 935-954.
[2] 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.
[3] 吴宝, 池仁勇. 融入情感分析与用户热度的社交网络用户可信度量方法[J]. 系统科学与数学, 2021, 41(4): 1091-1107.
[4] 闫晓雪, 纪志坚.  从Stackelberg-Nash均衡视角对动态社交网络系统中的意见分层建模分析[J]. 系统科学与数学, 2021, 41(11): 3029-3048.
[5] 顾秋阳,琚春华,鲍福光. 融入用户关系强度的社交网络舆情信源发现方法[J]. 系统科学与数学, 2020, 40(9): 1578-1596.
[6] 顾秋阳,琚春华,鲍福光. 融入用户群体行为的移动社交网络舆情传播动态演化模型研究[J]. 系统科学与数学, 2020, 40(12): 2278-2296.
阅读次数
全文


摘要