Network public opinion propagation model based on interest matching in multiple relationship social network

doi:10.3969/j.issn.0253-2778.2018.09.007

Abstract

Abstract: The relationship between user profiles and the data of microblog content in Sina microblog was obtained by programming and web crawler, and a variety of explicit or implicit relationships between microblog users were discovered by using data mining. On the basis of this, a semi-supervised user interest matching prediction algorithm was proposed. According to the individual state division method of compartment model, a network public opinion propagation model is constructed based on user interest matching through state transition analysis and inference of state transition probability. The results show that the model can well describe the laws of public opinion propagation in social networks, and reproduce the real propagation process of network public opinion in the social network from the perspective of complex networks.

Key words: public opinion propagation, propagation model, complex network, data mining, social networks

CLC Number:

TP391

SUN Gengxin, BIN Sheng. Network public opinion propagation model based on interest matching in multiple relationship social network[J]. Journal of University of Science and Technology of China, 2018, 48(9): 730-738.

References

［1］
SUDBURY A. The proportion of the population never hearing a rumour[J]. Journal of Applied Probability, 1985, 22(2): 443-446.
[2] ZANETTE D H. Critical Behavior of Propagation on Small-world Networks[J]. Physical Review E, 2001, 64(1): 901-907.
[3] ZANETTE D H. Dynamics of rumor propagation on small-world networks [J]. Physical Review E, 2002, 65(4): 1-9.
[4] MORENO Y, ZANETTE D H. Dynamics of rumor spreading in complex networks[J]. Physical Review E, 2004, 69(2): 130-137.
[5] LIU Z, LAI Y C, YE N. Propagation and immunization of infection on general networks with both homogeneous and heterogeneous components [J]. Physical Review E, 2003, 67(3):11-19.
[6] ZHOU J, LIU Z, LI B. Influence of network structure on rumor propagation[j]. Physics Letters A, 2007, 368(6): 458-463.
[7] KESTEN H, SIDORAVICIUS V. The spread of a rumor or infection in a moving population[J]. Annals of Probability, 2003, 33(6): 2402-2462.
[8] 潘灶烽, 汪小帆, 李翔. 可变聚类系数无标度网络上的谣言传播仿真研究[J]. 系统仿真学报, 2006,18(8): 2346-2348.
[9] 刘常昱, 胡晓峰, 司光亚,等. 基于小世界网络的舆论传播模型研究[J]. 系统仿真学报, 2006, 18(12): 3608-3610.
[10] 刘常昱, 胡晓峰, 罗批,等. 基于不对称人际影响的舆论涌现模型研究[J]. 系统仿真学报, 2008,20(4): 990-992.
[11] ZHOU T. A brief review of complex networks[J]. Physics, 2005, 34(1): 31-36.
[12] FANG J. Theoretical research progress in complexity of complex dynamical networks[J]. Progress in Natural Science, 2007,17(7): 841-857.
[13] FU F. Social dilemmas in an online social network: The structure and evolution of cooperation[J]. Physics Letters A, 2007, 371(1): 58-64.
[14] 张亚明, 唐朝生, 李伟钢. 在线社交网络谣言传播兴趣衰减与社会强化机制研究[J]. 情报学报, 2015,34(8): 833-844.
[15] LIU N, LU Y, TANG X, et al. Multi-document summarization algorithm based on significance topic of LDA[J]. Journal of Frontiers of Computer Science and Technology, 2015, 9(2): 242-248.
[16] YAN M, ZHANG X, YANG D，et al. A component recommender for bug reports using discriminative probability latent semantic analysis[J]. Information and Software Technology, 2016,73(3): 37-51.
[17] YU J, ZHU T. Combining long-term and short-term user interest for personalized hash tag recommendation [J]. Frontiers of Computer Science, 2015,9(4): 608-622.
[18] 张健沛, 姜延良. 一种基于节点相似性的链接预测算法[J]. 中国科技论文, 2013,8(7): 659-662.
[19] 孙翎, 迟嘉昱. 基于仓室模型的危机蔓延建模与演化分析[J]. 管理学报, 2010,7(12): 1878-1883.
[20] 张旸，路荣，杨青. 微博客中转发行为的预测研究[J]. 中文信息学报, 2012. 26(4): 109-114.
[21] 李志宏, 庄云蓓. 基于PageRank算法的双维度微博用户影响力实时度量模型[J]. 系统工程, 2016，7(2): 128-137.

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