IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v392y2013i9p2240-2247.html
   My bibliography  Save this article

A last updating evolution model for online social networks

Author

Listed:
  • Bu, Zhan
  • Xia, Zhengyou
  • Wang, Jiandong
  • Zhang, Chengcui

Abstract

As information technology has advanced, people are turning to electronic media more frequently for communication, and social relationships are increasingly found on online channels. However, there is very limited knowledge about the actual evolution of the online social networks. In this paper, we propose and study a novel evolution network model with the new concept of “last updating time”, which exists in many real-life online social networks. The last updating evolution network model can maintain the robustness of scale-free networks and can improve the network reliance against intentional attacks. What is more, we also found that it has the “small-world effect”, which is the inherent property of most social networks. Simulation experiment based on this model show that the results and the real-life data are consistent, which means that our model is valid.

Suggested Citation

  • Bu, Zhan & Xia, Zhengyou & Wang, Jiandong & Zhang, Chengcui, 2013. "A last updating evolution model for online social networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(9), pages 2240-2247.
    • Handle: RePEc:eee:phsmap:v:392:y:2013:i:9:p:2240-2247
    DOI: 10.1016/j.physa.2013.01.006
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378437113000204
    Download Restriction: Full text for ScienceDirect subscribers only. Journal offers the option of making the article available online on Science direct for a fee of $3,000
    File URL: https://libkey.io/10.1016/j.physa.2013.01.006?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Chen, Yiping & Paul, Gerald & Cohen, Reuven & Havlin, Shlomo & Borgatti, Stephen P. & Liljeros, Fredrik & Eugene Stanley, H., 2007. "Percolation theory and fragmentation measures in social networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 378(1), pages 11-19.
    2. Barabási, Albert-László & Albert, Réka & Jeong, Hawoong, 1999. "Mean-field theory for scale-free random networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 272(1), pages 173-187.
    3. Li, Xiang & Chen, Guanrong, 2003. "A local-world evolving network model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 328(1), pages 274-286.
    4. A. Barrat & M. Weigt, 2000. "On the properties of small-world network models," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 13(3), pages 547-560, February.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Zhan Bu & Chengcui Zhang & Zhengyou Xia & Jiandong Wang, 2014. "An FAR-SW based approach for webpage information extraction," Information Systems Frontiers, Springer, vol. 16(5), pages 771-785, November.
    2. Yuan, Wei-Guo & Liu, Yun, 2015. "A mixing evolution model for bidirectional microblog user networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 432(C), pages 167-179.
    3. Weiwei Yan & Xin Wen & Yin Zhang & Sonali Kudva & Qian Liu, 2023. "The dynamics of Q&A in academic social networking sites: insights from participants, interaction network, response time, and discipline differences," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(3), pages 1895-1922, March.
    4. Sun, Xin & Dong, Junyu & Tang, Ruichun & Xu, Mantao & Qi, Lin & Cai, Yang, 2015. "Topological evolution of virtual social networks by modeling social activities," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 433(C), pages 259-267.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Luo, Xiaojuan & Hu, Yuhen & Zhu, Yu, 2014. "Topology evolution model for wireless multi-hop network based on socially inspired mechanism," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 416(C), pages 639-650.
    2. Wen, Guanghui & Duan, Zhisheng & Chen, Guanrong & Geng, Xianmin, 2011. "A weighted local-world evolving network model with aging nodes," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(21), pages 4012-4026.
    3. Guan, Zhi-Hong & Wu, Zheng-Ping, 2008. "The physical position neighbourhood evolving network model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(1), pages 314-322.
    4. Huang, Xikun & Lu, Ruqian, 2020. "An evolving network model with information filtering and mixed attachment mechanisms," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 545(C).
    5. Zhu, Hailin & Luo, Hong & Peng, Haipeng & Li, Lixiang & Luo, Qun, 2009. "Complex networks-based energy-efficient evolution model for wireless sensor networks," Chaos, Solitons & Fractals, Elsevier, vol. 41(4), pages 1828-1835.
    6. Chen, Hailiang & Chen, Bin & Ai, Chuan & Zhu, Mengna & Qiu, Xiaogang, 2022. "The evolving network model with community size and distance preferences," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 596(C).
    7. Liang, Wei & Shi, Yuming & Huang, Qiuling, 2014. "Modeling the Chinese language as an evolving network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 393(C), pages 268-276.
    8. Konstantinos Antoniadis & Kostas Zafiropoulos & Vasiliki Vrana, 2016. "A Method for Assessing the Performance of e-Government Twitter Accounts," Future Internet, MDPI, vol. 8(2), pages 1-18, April.
    9. Yan Qiang & Bo Pei & Weili Wu & Juanjuan Zhao & Xiaolong Zhang & Yue Li & Lidong Wu, 2014. "Improvement of path analysis algorithm in social networks based on HBase," Journal of Combinatorial Optimization, Springer, vol. 28(3), pages 588-599, October.
    10. Yue Chen & Xiaojian Niu & Yan Zhang, 2019. "Exploring Contrarian Degree in the Trading Behavior of China's Stock Market," Complexity, Hindawi, vol. 2019, pages 1-12, April.
    11. Stephanie Rend'on de la Torre & Jaan Kalda & Robert Kitt & Juri Engelbrecht, 2016. "On the topologic structure of economic complex networks: Empirical evidence from large scale payment network of Estonia," Papers 1602.04352, arXiv.org.
    12. Gabrielle Demange, 2012. "On the influence of a ranking system," Social Choice and Welfare, Springer;The Society for Social Choice and Welfare, vol. 39(2), pages 431-455, July.
    13. Tsao, J.Y. & Boyack, K.W. & Coltrin, M.E. & Turnley, J.G. & Gauster, W.B., 2008. "Galileo's stream: A framework for understanding knowledge production," Research Policy, Elsevier, vol. 37(2), pages 330-352, March.
    14. Pier Paolo Saviotti, 2011. "Knowledge, Complexity and Networks," Chapters, in: Cristiano Antonelli (ed.), Handbook on the Economic Complexity of Technological Change, chapter 6, Edward Elgar Publishing.
    15. Duan, Shuyu & Wen, Tao & Jiang, Wen, 2019. "A new information dimension of complex network based on Rényi entropy," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 516(C), pages 529-542.
    16. Chung-Yuan Huang & Chuen-Tsai Sun & Hsun-Cheng Lin, 2005. "Influence of Local Information on Social Simulations in Small-World Network Models," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 8(4), pages 1-8.
    17. Wang, Jianrong & Wang, Jianping & Han, Dun, 2017. "Nonlinear dynamic evolution and control in CCFN with mixed attachment mechanisms," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 466(C), pages 120-132.
    18. Sun, Bingbin & Yao, Jialing & Xi, Lifeng, 2019. "Eigentime identities of fractal sailboat networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 520(C), pages 338-349.
    19. Yang, Xu-Hua & Lou, Shun-Li & Chen, Guang & Chen, Sheng-Yong & Huang, Wei, 2013. "Scale-free networks via attaching to random neighbors," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(17), pages 3531-3536.
    20. Colizza, Vittoria & Flammini, Alessandro & Maritan, Amos & Vespignani, Alessandro, 2005. "Characterization and modeling of protein–protein interaction networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 352(1), pages 1-27.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:phsmap:v:392:y:2013:i:9:p:2240-2247. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/physica-a-statistical-mechpplications/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.