The traditional information propagation model is more suitable for homogeneous network, and cannot be effectively applied to the non-homogeneous scale-free Social Network (SN). To solve this problem, an information propagation model based on local information was proposed. Topological characteristic difference between users and different effect on information propagation of user influence were considered in the model, and the probability of infection was calculated according to the neighbor nodes' infection and authority. Thus it could simulate the information propagation on real social network. By taking simulation experiments on Sina microblog networks, it shows that the proposed model can reflect the propagation scope and rapidity better than the traditional Susceptible-Infective-Recovered (SIR) model. By adjusting the parameters of the proposed model, it can verify the impact of control measures to the propagation results.
程晓涛, 刘彩霞, 刘树新. 基于局域信息的社交网络信息传播模型[J]. 计算机应用, 2015, 35(2): 322-325.
CHENG Xiaotao, LIU Caixia, LIU Shuxin. Information propagation model for social network based on local information. Journal of Computer Applications, 2015, 35(2): 322-325.
[1] Wikipedia [EB/OL].[2014-06-23]. http://en.wikipedia.Org//wiki. [2] WATTS D J, STROGATZ S H. Collective dynamics of ‘small-world’ networks [J]. Nature, 1998, 393(6684): 440-442. [3] BARABSI A L, ALBERT R, JEONG H. Mean-field theory for scale-free random networks [J]. Physica A: Statistical Mechanics and Its Applications, 1999, 272(1): 173-187. [4] ZHANG Y, LIU Y, ZHANG H, et al. The research of information dissemination model on online social network [J]. Acta Physica Sinica, 2011, 60(5): 501-507. (张彦超,刘云,张海峰,等. 基于在线社交网络的信息传播模型[J]. 物理学报, 2011, 60(5): 501-507.) [5] WANG H, HAN J, DENG L, et al. Dynamics of rumor spreading in mobile social networks [J]. Acta Physica Sinica, 2013, 62(11): 106-117. (王辉,韩江洪,邓林,等.基于移动社交网络的谣言传播动力学研究[J]. 物理学报, 2013, 62(11): 106-117.) [6] ZHENG M, LYU L, ZHAO M. Spreading in online social networks: the role of social reinforcement [J]. Physical Review E, 2013, 88(1): 012818. [7] BAKSHY E, ROSENN I, MARLOW C, et al. The role of social networks in information diffusion [C]//WWW 2012: Proceedings of the 21st International Conference on World Wide Web. New York: ACM, 2012: 519-528. [8] MENG Z, FU X. Dynamic information spreading model based on online social network [J]. Journal of Computer Applications, 2014, 34(7): 1960-1963.(蒙在桥,傅秀芬. 基于在线社交网络的动态消息传播模型[J]. 计算机应用,2014,34(7): 1960-1963.) [9] LI D, XU Z, LI S, et al. A survey on information diffusion in online social networks [J]. Chinese Journal of Computers, 2014, 37(1): 189-206. (李栋,徐志明,李生,等.在线社会网络中信息扩散[J].计算机学报,2014,37(1):189-206.) [10] YANG Z, GUO J, CAI K, et al. Understanding retweeting behaviors in social networks [C]//CIKM 2010: Proceedings of the 19th ACM International Conference on Information and Knowledge Management. New York: ACM, 2010: 1633-1636. [11] TANG S, YUAN J, MAO X, et al. Relationship classification in large scale online social networks and its impact on information propagation [C]//INFOCOM 2011: Proceedings of the 30th IEEE International Conference on Computer Communications. New York: IEEE Communications Society, 2011: 2291-2299. [12] WANG X, LI X, CHEN G. The introduction of network science [M]. Beijing: Higher Education Press, 2012: 306. (汪小帆,李翔,陈关荣. 网络科学导论[M]. 北京:高等教育出版社, 2012: 306.) [13] KARRER B, NEWMAN M E J. Message passing approach for general epidemic models [J]. Physical Review E, 2010, 82(1): 016101. [14] LOKHOV A Y, MZARD M, OHTA H, et al. Inferring the origin of an epidemic with dynamic message-passing algorithm [J]. Physical Review E, 2013, 90(1): 012801. [15] WANG X, LI X, CHEN G. The theory and application of complex networks [M]. Beijing: Tsinghua University Press, 2006: 56. (汪小帆,李翔,陈关荣. 复杂网络理论及其应用[M]. 北京:清华大学出版社, 2006: 56.) [16] YANG X. The research for unexpected network rumors' generation and response mechanism [J]. Southeast Communication, 2014(1): 101-103. (杨小林. 突发事件网络谣言的产生机理与应对机制研究[J]. 东南传播, 2014(1): 101-103.)