American Institute of Mathematical Sciences

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doi: 10.3934/jimo.2019067

Rumor propagation controlling based on finding important nodes in complex network

Jianfeng Jia 1, , Xuewei Liu 2, , Yixin Zhang 2,, , Zhe Li 2, , Yanjie Xu 2, and  Jiaqi Yan 3,

1. 

School of Business Administration, Northeastern University, Shenyang 110169, China
2. 

Software College, Northeastern University, Shenyang 110169, China
3. 

School of Economics and Management, Tongji University, Shanghai 200092, China

* Corresponding author: Yixin Zhang

Received  October 2018 Revised  December 2018 Published  July 2019

The rumor propagation analysis and important nodes detection is a hot topic in complex network under crisis situation. The traditional propagation model does not consider enough states, so it cannot intact reflect the real world. In this paper, a new rumor propagation model which considers the Wiseman and the Truth Spreader is proposed based on the Graph Theory. Then, 3 new methods are proposed to find important nodes in the new model. These methods consider the differences between nodes to evaluate the importance of the nodes. Finally, 4 networks are demonstrated to show that the 3 proposed methods are useful to control rumor propagation.

Keywords: Rumor propagation, complex network, important nodes, propagation model.
Mathematics Subject Classification: 05C82.
Citation: Jianfeng Jia, Xuewei Liu, Yixin Zhang, Zhe Li, Yanjie Xu, Jiaqi Yan. Rumor propagation controlling based on finding important nodes in complex network. Journal of Industrial & Management Optimization, doi: 10.3934/jimo.2019067
References:
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Z. F. Pan, X. F. Wang and X. Li, Simulation investigation on rumor spreading on scale-free network with tunable clustering, Journal of System Simulation, 18 (2006), 2346-2348.   Google Scholar

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T. Ren, Y. F. Wang, D. Du, M. M. Liu and A. Siddiqi, The guitar chord-generating algorithm based on complex network, Physica A, 443 (2016), 1-13.  doi: 10.1016/j.physa.2015.09.041.  Google Scholar

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J. Wei, B. Bu and L. Liang, Estimating the diffusion models of crisis information in micro blog, Journal of Informatics, 6 (2012), 600-610.  doi: 10.1016/j.joi.2012.06.005.  Google Scholar

[17]

H. Xie, Y. Yan and Y. Hou, Dynamical behavior of rumor in online social networks, International Journal of Multimedia and Ubiquitous Engineering, 11 (2016), 125-132.  doi: 10.14257/ijmue.2016.11.3.12.  Google Scholar

[18]

D. H. Zanette, Dynamics of rumor propagation on small-world networks, Physical Review E, 65 (2002), 1464-3634.  doi: 10.1103/PhysRevE.65.041908.  Google Scholar

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J. Zeng, C. H. Chan and K. W. Fu, How social media construct "truth" around crisis events: Weibo's rumor management strategies after the 2015 Tianjin blasts, Policy & Internet, in press, (2017). doi: 10.1002/poi3.155.  Google Scholar

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Z. Zhu and Y. Liu, Simulation study of propagation of rumor in online social network based on scale-free network with tunable clustering, Complex Systems & Complexity Science, 13 (2016), 74-82.   Google Scholar

show all references

References:
[1]

K. Berahmand, A. Bouyer and N. Samadi, A new centrality measure based on the negative and positive effects of clustering coefficient for identifying influential spreaders in complex networks, Chaos, 110 (2018), 41-54.   Google Scholar

[2]

D. B. Chen, L. Y. Lv, M. S. Shang, C. Yi and T. Zhou, Identifying influential nodes in complex networks, Physica A, 391 (2012), 1777-1787.  doi: 10.1016/j.physa.2011.09.017.  Google Scholar

[3]

D. J. Daley and D. G. Kendall, Epidemics and rumours, Nature, 204 (1964), 1464-3634.  doi: 10.1038/2041118a0.  Google Scholar

[4]

R. Granizo, F. R. Blanquez, E. Rebollo and C. A. Platero, A novel ground fault non-directional selective protection method for ungrounded distribution networks, Energies, 8 (2015), 1291-1316.  doi: 10.3390/en8021291.  Google Scholar

[5]

V. L. M. Huszar, J. C. Nabout, M. O. Appel, J. B. O. Santos, D. S. Abe and L. H. S. Silva, Environmental and not spatial processes (directional and non-directional) shape the phytoplankton composition and functional groups in a large subtropical river basin, Journal of Plankton Research, 660 (2015), 1190-1200.  doi: 10.1093/plankt/fbv084.  Google Scholar

[6]

M. Kitsak, L. K. Gallos, S. Havlin and F. Liljeros, Identifying influential spreaders in complex networks, Nature, 6 (2010), 888-893.   Google Scholar

[7]

D. Li and J. Ma, How the government's punishment and individual's sensitivity affect the rumor spreading in online social networks, Physica A, 46 (2017), 284-292.  doi: 10.1016/j.physa.2016.11.033.  Google Scholar

[8]

Y. Liu, B. Wei, Y. X. Du, F. Y. Xiao and Y. Deng, Identifying inflential spreaders by weight degree centrality in complex networks, Chaos, 86 (2016), 1-7.  doi: 10.1016/j.chaos.2016.01.030.  Google Scholar

[9]

Y. Moreno, M. Nekovee and A. F. Pacheco, Dynamics of rumor spreading in complex networks, Physical Review E, 69 (2004), 1464-3634.  doi: 10.1103/PhysRevE.69.066130.  Google Scholar

[10]

Z. F. Pan, X. F. Wang and X. Li, Simulation investigation on rumor spreading on scale-free network with tunable clustering, Journal of System Simulation, 18 (2006), 2346-2348.   Google Scholar

[11]

T. Ren, Y. F. Wang, D. Du, M. M. Liu and A. Siddiqi, The guitar chord-generating algorithm based on complex network, Physica A, 443 (2016), 1-13.  doi: 10.1016/j.physa.2015.09.041.  Google Scholar

[12]

T. Ren, Y. F. Wang, M. M. Liu and Y. J. Xu, Analysis of robustness of urban bus network, Chinese Physics B, 25 (2016).  doi: 10.1088/1674-1056/25/2/020101.  Google Scholar

[13]

Y. Sun, Y. Ma, F. Zhang, Y. Ma and W. Shen, Key nodes discovery in large-scale logistics network based on MapReduce, IEEE International Conference on Systems, (2016), 1309-1314.  doi: 10.1109/SMC.2015.233.  Google Scholar

[14]

Z. H. Tan, J. Y. Ning, Y. Liu, X. W. Wang, G. M. Yang and W. Yang, ECR Model: An elastic collision-based rumor-propagation model in online social networks, IEEE Access, 4 (2016), 6105-6120.   Google Scholar

[15]

B. X. Wang, Y. F. Wen, P. F. Ma and P. Hu, A Dynamic-TDMA MAC mechanism for directional networks with a central node, Radio Engineering, (2015), 24-29.   Google Scholar

[16]

J. Wei, B. Bu and L. Liang, Estimating the diffusion models of crisis information in micro blog, Journal of Informatics, 6 (2012), 600-610.  doi: 10.1016/j.joi.2012.06.005.  Google Scholar

[17]

H. Xie, Y. Yan and Y. Hou, Dynamical behavior of rumor in online social networks, International Journal of Multimedia and Ubiquitous Engineering, 11 (2016), 125-132.  doi: 10.14257/ijmue.2016.11.3.12.  Google Scholar

[18]

D. H. Zanette, Dynamics of rumor propagation on small-world networks, Physical Review E, 65 (2002), 1464-3634.  doi: 10.1103/PhysRevE.65.041908.  Google Scholar

[19]

J. Zeng, C. H. Chan and K. W. Fu, How social media construct "truth" around crisis events: Weibo's rumor management strategies after the 2015 Tianjin blasts, Policy & Internet, in press, (2017). doi: 10.1002/poi3.155.  Google Scholar

[20]

Z. Zhu and Y. Liu, Simulation study of propagation of rumor in online social network based on scale-free network with tunable clustering, Complex Systems & Complexity Science, 13 (2016), 74-82.   Google Scholar

Figure 1.  The proposed rumor propagation model
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Figure 2.  The number of Gullible, Spreaders and Truth Spreaders in BA network
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Figure 3.  Comparison of node importance
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Figure 4.  An example of IPA
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Figure 5.  The number of spreaders in BA scale-free network
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Figure 6.  The number of spreaders in ER network
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Figure 7.  The number of spreaders in Facebook network
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Figure 8.  The number of spreaders in E-mail communication network
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