Validity Analysis of Network Big Data

Authors

  • Peng Wang School of Economics and Management, Weifang University of Science and Technology, Shandong, China
  • Huaxia Lv School of Economics and Management, Weifang University of Science and Technology, Shandong, China
  • Xiaojing Zheng School of Economics and Management, Weifang University of Science and Technology, Shandong, China
  • Wenhui Ma School of Economics and Management, Weifang University of Science and Technology, Shandong, China
  • Weijin Wang School of International Business, Shandong College of Economics and Business, Shandong, China

DOI:

https://doi.org/10.13052/jwe1540-9589.2234

Keywords:

Network big data, system dynamics, critical point, scale-free network, F-J model

Abstract

False data in network big data has led to considerable ineffectiveness in perceiving the property of fact. Correct conclusions can be drawn only by accurately identifying and eliminating these false data. In other words, analysis is the premise to reaching a correct conclusion. This paper develops a big data network dissemination model based on the properties of the network. We also analyze the attributes of the big data random complex network based on the revised F-J model. Then, based on the scale-free nature of network big data, the evolution law of connected giant components and Bayesian inference, we propose an identification method of effective data in networks. Finally, after obtaining the real data, we analyze the dissemination and evolution characteristics of the network big data. The results show that if some online users intentionally spread false data on a large-scale website, the entire network data becomes false, despite a minimal probability of choosing these dissemination sources.

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Author Biographies

Peng Wang, School of Economics and Management, Weifang University of Science and Technology, Shandong, China

Peng Wang has a Ph.D. in management, and graduated from Korea Woosong University in 2018. She is a lecturer for the School of Economic and Management in Weifang University of Science and Technology. She has been engaged in research work on unstructural data modeling in management system, estimation and controlling for 5 years. She has been involved in several corresponding scientific projects. She is good at modeling and analyzing management complex systems from a complex system perspective. In recent years, she has begun to identify risk in a management complex system by multi-agent modeling, and identification, estimation and control.

Huaxia Lv, School of Economics and Management, Weifang University of Science and Technology, Shandong, China

Huaxia Lv is a master of region economics, graduated from Henan University in 2007. She is a lecturer for the School of Economic and Management in Weifang University of Science and Technology. She has engaged in research work on spatial patterns of regional economy and its evolution for almost 10 years, and has published approximate 10 papers in different journals. In recent years, she has begun to pay attention to the research field of individual behavior and collective behavior and has developed a strong interest, obtaining some achievements. She will continue her research work in this area. She is good at data analysis and application.

Xiaojing Zheng, School of Economics and Management, Weifang University of Science and Technology, Shandong, China

Xiaojing Zheng has a Ph.D. in management science and engineering, and graduated from Wuhan University in 2012. He is a Professor of the School of Economic and Management in Weifang University of Science and Technology. He has engaged in research work on complex adaptive systems of management and supply chain coordination and risk analysis for almost 15 years, and has published approximate 50 papers in different journals. He is good at constructing various models for management complex adaptive systems by multi-agent modeling. In recent years, he has focused on the emergence of collective behavior driven by irrational individual behavior, consisting of invariable distribution, self-similarity, criticality and percolation of the collective; the corresponding research results has been published in several important scientific journals, which have gained the recognition of other scientists.

Wenhui Ma, School of Economics and Management, Weifang University of Science and Technology, Shandong, China

Wenhui Ma is a master of supply chain management, and graduated from Harbin University of Commerce in 2009. She is a lecturer in the School of Economic and Management in Weifang University of Science and Technology. She has engaged in research work on supply chain coordination based on unstructural data for 5 years. She has been involved in several corresponding scientific projects. She is good at unstructural data modeling in a management system, supply chain modeling, coordination and controlling from supply chain coordination. In recent years, she has begun unstructural data modeling in a management system, supply chain modeling, coordination and controlling.

Weijin Wang, School of International Business, Shandong College of Economics and Business, Shandong, China

Weijin Wang is an M.D. of Economics, and graduated from Shandong University in 2007. He is an associate professor of the School of International Trade in Shandong College of Economics and Business. He has engaged in research work on international trade and agriculture economy for 13 years, and has published several papers in journals and several books. He has headed up and been involved in several corresponding scientific projects. He is good at economic systems in qualitative analysis from a philosophical perspective. In recent years, he has been researching the phenomenon and the evolution law of macroecnomics by introducing unstructural data, and has achieved some modest conclusions.

References

A. Sahaym, et al., “Mixed blessings: how top management team heterogeneity and governance structure influence the use of corporate venture capital by post-IPO firms”, Journal of Business Research, vol. 69, no. 3, pp. 1208–1218, 2016.

A. N. Tump, T. J. Pleskac, R. H. J. M. Kurvers, “Wise or mad crowds? The cognitive mechanisms underlying information cascades”, Science Advances, vol. 6, no. 29, pp. 1–11, 2020.

B. Bollobás, O. Riordan, “Robustness and vulnerability of scale-free random graphs”, Internet Mathematics, vol. 1, no. 1, pp. 1–35, 2007.

B. Bollobás, O. Riordan, “Sparse graphs: metrics and random models”, Random Structures & Algorithms, vol. 39, no. 1, pp. 1–38, 2011.

B. Bollobás, et al., Handbook of Large-Scale Random Networks. Springer, 2008.

A. S. Boccaletti, et al., “Complex networks: Structure and dynamics”, Physics Reports, vol. 424, no. 4–5, pp. 175–308, 2006.

D. Hume, “Cause and Effect, Indianapolis, 1772”, An Enquiry Concerning Human Understanding. Hackett Publishing Company, 1993.

M. H. DeGroot, “Reaching a Consensus,” Journal of the American Statal Association, vol. 69, no. 345, pp. 118–121, 1974.

T. Dixon, “Emotion: The history of a keyword in crisis”, Emotion Review, vol. 4, no. 4, pp. 338–344, 2012.

D. S. Callway, et al., “Network robustness and fragility: Percolation on random graphs”, Physical Review Letters, vol. 85, no. 25, pp. 5468–5471, 2000.

N. E. Friedkin, et al., “A theory of the evolution of social power: Natural trajectories of interpersonal influence systems along issue sequences”, Sociological Science, vol. 3, no. 20, pp. 444—472, 2016.

N. E. Friedkin, et al., “Network science on belief system dynamics under logic constraints”, Science, vol. 354, no. 6310, pp. 321–326, 2016.

L. Guo, J. Ljung, G. J. Wang, “Necessary and sufficient conditions for stability of LMS”, IEEE Transactions on Automatic Control, vol. 42, no. 6, pp. 761–770,1997.

I. Kozic, “Role of symmetry in irrational choice”, Preprint, arXiv:1806.02627, 2020. https://arxiv.org/abs/1806.02627.

J. Q. Cao, “Application of overlapping community discovery algorithm in complex network big data”, Automatic Control and Computer Sciences, vol. 55, pp. 8–15, 2021.

K. Hu, T. Hu, Y. Tang, “Cascade defense via control of the fluxes in complex networks”, Journal of Statistical Physics, vol. 141, pp. 555–565, 2010.

D. Lazer, et al., “The parable of Google Flu: traps in big data analysis”, Science, vol. 343, pp. 1203–1205, 2010.

I. Leyva, “Entrainment competition in complex networks”, International Journal of Bifurcation and Chaos, vol. 20, no. 3, pp. 827–833, 2010.

H. X. Lv, X. J. Zheng, S. Chen, “Reveal the pattern of causality in processes of urbanization and economic growth. an evidence from China”, Scientific Programming, vol. 2725113, pp. 1–17, 2022.

S. A. Masato, “Functional advantages of Lévy walks emerging near a critical point”, PNAS, vol. 117, no. 39, pp. 24336–24344, 2020.

S. E. Parsegov, “Novel multidimensional models of opinion dynamics in social networks”, IEEE Transactions on Automatic Control, vol. 62, no. 5, pp. 2270–2285, 2017.

P. Ball, Critical Mass – How One Thing Leads to Another. New York: Farrar, Straus and Giroux, 2007.

A. M. Smith, et al., “Competitive percolation strategies for network recovery”, Scientific Reports, vol. 9, 11843, 2019.

L. Tai, L. Li, J. Du, “Multimedia based intelligent network big data optimization model”, Multimedia Tools amd Applications, vol. 78, no. 4, pp. 4579–4603, 2019.

T. Jäger, A. Passeggi, “On Torus Homeomorphisms Semiconjugate to irrational Rotations”, Ergodic Theory and Dynamical Systems, vol. 35, no. 7, pp. 2114–2137, 2015.

V. Marx, “The big challenges of big data”, Nature, vol. 498, no. 7453, pp. 255–260, 2013.

X. J. Zheng, et al., “Random belief system dynamics in complex networks under time-varying logic constraints”, Physica A: Statistical Mechanics and its Applications, vol. 566:125552, 2020.

X. J. Zheng, J. J. Zheng, “Criticality of symmetry breaking of collective behavior driven by individual rules”, System Engineering Theory and Practice, vol. 36, no. 2, pp. 413–426, 2016.

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Published

2023-07-03

How to Cite

Wang, P. ., Lv, H. ., Zheng, X. ., Ma, W. ., & Wang, W. . (2023). Validity Analysis of Network Big Data. Journal of Web Engineering, 22(03), 465–496. https://doi.org/10.13052/jwe1540-9589.2234

Issue

2023: Vol 22 Iss 3

Section

Advanced Practice in Web Engineering