September  2020, 16(5): 2521-2529. doi: 10.3934/jimo.2019067

Rumor propagation controlling based on finding important nodes in complex network

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  September 2020 Early access  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.

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 and Management Optimization, 2020, 16 (5) : 2521-2529. doi: 10.3934/jimo.2019067
References:
[1]

K. BerahmandA. 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. 

[2]

D. B. ChenL. Y. LvM. S. ShangC. Yi and T. Zhou, Identifying influential nodes in complex networks, Physica A, 391 (2012), 1777-1787.  doi: 10.1016/j.physa.2011.09.017.

[3]

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

[4]

R. GranizoF. R. BlanquezE. 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.

[5]

V. L. M. HuszarJ. C. NaboutM. O. AppelJ. B. O. SantosD. 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.

[6]

M. KitsakL. K. GallosS. Havlin and F. Liljeros, Identifying influential spreaders in complex networks, Nature, 6 (2010), 888-893. 

[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.

[8]

Y. LiuB. WeiY. X. DuF. 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.

[9]

Y. MorenoM. 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.

[10]

Z. F. PanX. 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. 

[11]

T. RenY. F. WangD. DuM. 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.

[12]

T. RenY. F. WangM. 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.

[13]

Y. SunY. MaF. ZhangY. 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.

[14]

Z. H. TanJ. Y. NingY. LiuX. W. WangG. M. Yang and W. Yang, ECR Model: An elastic collision-based rumor-propagation model in online social networks, IEEE Access, 4 (2016), 6105-6120. 

[15]

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

[16]

J. WeiB. 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.

[17]

H. XieY. 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.

[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.

[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.

[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. 

show all references

References:
[1]

K. BerahmandA. 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. 

[2]

D. B. ChenL. Y. LvM. S. ShangC. Yi and T. Zhou, Identifying influential nodes in complex networks, Physica A, 391 (2012), 1777-1787.  doi: 10.1016/j.physa.2011.09.017.

[3]

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

[4]

R. GranizoF. R. BlanquezE. 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.

[5]

V. L. M. HuszarJ. C. NaboutM. O. AppelJ. B. O. SantosD. 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.

[6]

M. KitsakL. K. GallosS. Havlin and F. Liljeros, Identifying influential spreaders in complex networks, Nature, 6 (2010), 888-893. 

[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.

[8]

Y. LiuB. WeiY. X. DuF. 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.

[9]

Y. MorenoM. 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.

[10]

Z. F. PanX. 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. 

[11]

T. RenY. F. WangD. DuM. 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.

[12]

T. RenY. F. WangM. 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.

[13]

Y. SunY. MaF. ZhangY. 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.

[14]

Z. H. TanJ. Y. NingY. LiuX. W. WangG. M. Yang and W. Yang, ECR Model: An elastic collision-based rumor-propagation model in online social networks, IEEE Access, 4 (2016), 6105-6120. 

[15]

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

[16]

J. WeiB. 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.

[17]

H. XieY. 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.

[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.

[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.

[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. 

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