Dynamic information spreading model based on online social network

doi:10.11772/j.issn.1001-9081.2014.07.1960

Journal of Computer Applications ›› 2014, Vol. 34 ›› Issue (7): 1960-1963.DOI: 10.11772/j.issn.1001-9081.2014.07.1960

• Network and communications • Previous Articles Next Articles

Dynamic information spreading model based on online social network

MENG Zaiqiao,FU Xiufen

School of Computer, Guangdong University of Technology, Guangzhou Guangdong 510006, China

Received:2014-01-26 Revised:2014-02-27 Online:2014-08-01 Published:2014-07-01
Contact: MENG Zaiqiao

基于在线社交网络的动态消息传播模型

蒙在桥,傅秀芬

广东工业大学计算机学院，广州 510006

通讯作者: 蒙在桥
作者简介:蒙在桥(1988-)，男，江西赣州人，硕士研究生，主要研究方向:复杂网络、数据挖掘；傅秀芬(1957-)，女，福建漳州人，教授，主要研究方向:网络多媒体、协同软件、网络安全。
基金资助:
广东省自然科学基金资助项目

Abstract

Abstract:

Traditional spreading models have difficulties in descripting the complex activity patterns and the topological differences between nodes in online social networks, and the contact-based spreader annihilation mechanisms in these models do not fit with the reality. To filling the gap between spreading simulations of theoretical model and realities of information spreading, a new dynamic information spreading model (D-SIR) based on online social network was proposed. With consideration of some practical factors in information dissemination process, this model introduced the time delay annihilation mechanism that spreaders changed to stiflers spontaneously and the dynamic authority and resistance of nodes mechanism to apply to inhomogeneous networks, and considered the receiving reinforced signal effect and the social reinforcement. With the variances of parameters, the simulations on the real-world online social network which is constructed by crawled Sina microblog data verify that D-SIR model can reflect the real spreading situation in online social network. And compared to the traditional spreading model, the new model is more flexible and extensible.

摘要：

传统传播模型较难描述在线社交网络中的复杂活跃模式以及节点间的拓扑差异，并且其接触式的传播者退化方式也与现实不符。针对理论模型模拟与现实消息传播的不符，提出一个基于在线社交网络的动态消息传播模型D-SIR。该模型考虑了在线社交网络中影响消息传播的一些实际因素，引入基于传播延迟的退化方式使传播者自发地退化成免疫者，动态指定节点的权威度和免疫力以适应非均质网络，并考虑接收增强信号效应以及外部社会加强效果。在采集的新浪微博真实传播网络数据中，通过参数变化的传播仿真实验验证了D-SIR模型可以有效反映在线社交网络的现实传播情形，并且较传统模型更具灵活性及可扩展性。

CLC Number:

TP391.9

MENG Zaiqiao FU Xiufen. 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.

References

[1]ZHAO L, WANG J, CHEN Y, et al. SIHR rumor spreading model in social networks［J］. Physica A, 2012, 391(7): 2444-2453.
[2]MORENO Y, NEKOVEE M, PACHECO A F. Dynamics of rumor spreading in complex networks［J］. Physical Review E, 2004, 69(6): 066130.

[3]NEKOVEE M, MORENO Y, BIANCONI G, et al. Theory of rumour spreading in complex social networks［J］. Physica A, 2007, 374(1): 457-70.
[4]BORGE-HOLTHOEFER J, MELONI S, GONALVES B, et al. Emergence of influential spreaders in modified rumor models［J］. Journal of Statistical Physics, 2013, 151(1/2): 383-393.
[5]ZHAO L, QIU X, WANG X, et al. Rumor spreading model considering forgetting and remembering mechanisms in inhomogeneous networks［J］. Physica A, 2012, 392(4): 987-94.
[6]LYU L, CHEN D B, ZHOU T. The small world yields the most effective information spreading［J］. New Journal of Physics, 2011, 13(12): 123005.
[7]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): 50501. (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): 50501.)
[8]WANG H, HAN J, DENG L, et al. Dynamics of rumor spreading in mobile social networks［J］. Acta Physica Sinica, 2013, 62(11): 110505.(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): 110505.)
[9]ZHAO L, XIE W, GAO H O, et al. A rumor spreading model with variable forgetting rate［J］. Physica A, 2013, 392(23): 6146-6154.
[10]XU X J, CHEN G. The SIS model with time delay on complex networks［J］. International Journal of Bifurcation and Chaos, 2009, 19(2): 623-628.
[11]MISLOVE A, MARCON M, GUMMADI K P, et al. Measurement and analysis of online social networks［C］// Proceedings of the 7th ACM SIGCOMM Conference on Internet Measurement. New York: ACM, 2007: 29-42.
[12]PERRA N, GONALVES B, PASTOR-SATORRAS R, et al. Activity driven modeling of time varying networks［J］. Scientific Reports, 2012, 2: Article no. 469.
[13]BORGE-HOLTHOEFER J, MORENO Y. Absence of influential spreaders in rumor dynamics［J］. Physical Review E, 2012, 85(2): 026116.
[14]LIN C X, MEI Q, HAN J, et al. The joint inference of topic diffusion and evolution in social communities［C］// Proceedings of the 11th IEEE International Conference on Data Mining. Piscataway: IEEE, 2011: 378-387.
[15]CENTOLA D. The spread of behavior in an online social network experiment［J］. Science, 2010, 329(5996): 1194-1197.
[16]NEWMAN M E. Power laws, Pareto distributions and Zipf's law［J］. Contemporary Physics, 2005, 46(5): 323-351.

Dynamic information spreading model based on online social network

基于在线社交网络的动态消息传播模型

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