A Weibo local network growth model constructed from the perspective of following-followed

doi:10.1088/1674-1056/ad84c4

Abstract In order to explore the evolution process of the Weibo local network, this study first defines four factors influencing the evolution of the Weibo network. On this basis, the BA scale-free network model was enhanced by incorporating these four factors and accounting for directionality, resulting in a Weibo local network evolution model based on user attributes and behavioral similarity. The model's validity was validated by comparing simulation results with real data. The findings indicate that the Weibo local network exhibits both small-world characteristics and distinctive features. The results show that the Weibo local network exhibits both small-world characteristics and distinctive properties. The in-degree distribution follows a mixed pattern of exponential and power-law distributions, the degree-degree shows isomatching, and both the in-degree centrality and eigenvector centrality values are relatively low. This research contributes to our understanding of user behaviour in the Weibo network, and provides a structural basis for exploring the impact of Weibo network structure on information dissemination.

Keywords: Weibo network user similarity attribute characteristics network evolution topology analysis

Received: 09 July 2024
Revised: 05 September 2024
Accepted manuscript online: 09 October 2024

PACS:	87.23.Ge	(Dynamics of social systems)
	87.23.Kg	(Dynamics of evolution)
	05.90.+m	(Other topics in statistical physics, thermodynamics, and nonlinear dynamical systems)
	89.75.-k	(Complex systems)

Fund: Project supported by the National Natural Science Foundation of China (Grant Nos. 62266030 and 61863025).

Corresponding Authors: Fu-Zhong Nian
E-mail: gdnfz@lut.edu.cn

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Fu-Zhong Nian(年福忠) and Ran-Qing Yao(姚然庆) A Weibo local network growth model constructed from the perspective of following-followed 2024 Chin. Phys. B 33 128702

[1] Watts D J and Strogatz S H 1998 Nature 393 440
[2] Barabási A L and Albert R 1999 Science 286 509
[3] Dorogovtsev S N, Mendes J F F and Samukhin A N 2000 Phys. Rev. Lett. 85 4633
[4] Li X and Chen G 2023 Physica A 328 274
[5] Bianconi G and Barabási A L 2001 Europhys. Lett. 54 436
[6] Han D, Qian J and Ma Y 2011 Europhys. Lett. 94 28006
[7] Zou Z Y, Liu P, Lei L and Gao J Z 2022 Chin. Phys. B 21 028904
[8] Guo J L 2014 Chin. Phys. B 23 070206
[9] Ran Y, Liu T, Jia T and Xu X K 2022 Chin. Phys. B 31 068902
[10] Qian J H and Han D D 2009 Physica A 388 4248
[11] Wasserman S and Faust K 1994 Social network analysis: Methods and applications
[12] Newman M E and Park J 2013 Phys. Rev. E 68 036122
[13] Junju L and Jian W 2014 IEEE 5th International Conference on Software Engineering and Service Science, Beijing, China, 2014, pp. 983- 986
[14] Guo J L, Guo Z H and Liu X J 2011 Chin. Phys. B 20 118902
[15] Wu Y, Xiao J H,Wu Z Y, Yang J Z and Ma B J 2007 Acta Phys. Sin. 56 2037 (in Chinese)
[16] Ahn Y Y, Han S, Kwak H, Moon S and Jeong H 2007 In Proceedings of the 16th international conference on World Wide Web (WWW ’07). Association for Computing Machinery, New York, NY, USA, pp. 835-844
[17] Mislove A, Marcon M, Gummadi K P, Druschel P and Bhattacharjee B 2007 Proceedings of the 7th ACM SIGCOMM conference on Internet measurement (IMC ’07). Association for Computing Machinery, New York, NY, USA, pp. 29-42
[18] Fu F, Liu L and Wang L 2008 Physica A 387 675
[19] Traud A L, Mucha P J and Porter M A 2012 Physica A 391 4165
[20] Xiong X, CaoW, Zhou X and Hu Y 2012 Journal of Sichuan University (Engineering Science Edition) 44 140
[21] Kumar R, Novak J and Tomkins A 2006 Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining pp. 611-617
[22] Xiong F, Liu Y, Si X and Ding F 2010 Acta Phys. Sin. 59 6889 (in Chinese)
[23] Bu Z, Xia Z, Wang J and Zhang C 2013 Physica A 392 2240
[24] Hu H B, Guo J L and Chen J 2012 Chin. Phys. B 21 118902
[25] Java A, Song X, Finin T and Tseng B 2007 Proceedings of the 9th WebKDD and 1st SNA-KDD 2007 workshop on Web mining and social network analysis pp. 56-65
[26] Kwak H, Lee C, Park H and Moon S 2010 Proceedings of the 19th international conference on World wide web pp. 591-600
[27] Yan Q, Wu L and Zheng L 2013 Physica A 392 1712
[28] Junju L and Jian W 2014 2014 IEEE 5th International Conference on Software Engineering and Service Science (IEEE), Beijing, China pp. 983-986
[29] Tang X, Miao Q, Quan Y, Tang J and Deng K 2015 Knowledge-Based Systems 89 681
[30] Nian F, Shi Y and Dang Z 2020 Int. J. Mod. Phys. B 34 2050323
[31] Nian F, Luo L and Liu X 2023 IEEE Transactions on Computational Social Systems 11 1967
[32] Nian F, Ren J and Yu X 2023 IEEE Transactions on Computational Social Systems 11 1277
[33] Yu X, Nian F, Yao Y and Luo L 2021 IEEE Transactions on Computational Social Systems 8 1143
[34] Nian F and Diao H 2019 IEEE Transactions on Network Science and Engineering 7 1394
[35] Zhang X, Han D D, Yang R and Zhang Z 2017 PloS one 12 e0183290
[36] Yuan W G and Liu Y 2015 Physica A 432 167

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