IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v484y2017icp394-411.html
   My bibliography  Save this article

The data-driven null models for information dissemination tree in social networks

Author

Listed:
  • Zhang, Zhiwei
  • Wang, Zhenyu

Abstract

For the purpose of detecting relatedness and co-occurrence between users, as well as the distribution features of nodes in spreading path of a social network, this paper explores topological characteristics of information dissemination trees (IDT) that can be employed indirectly to probe the information dissemination laws within social networks. Hence, three different null models of IDT are presented in this article, including the statistical-constrained 0-order IDT null model, the random-rewire-broken-edge 0-order IDT null model and the random-rewire-broken-edge 2-order IDT null model. These null models firstly generate the corresponding randomized copy of an actual IDT; then the extended significance profile, which is developed by adding the cascade ratio of information dissemination path, is exploited not only to evaluate degree correlation of two nodes associated with an edge, but also to assess the cascade ratio of different length of information dissemination paths. The experimental correspondences of the empirical analysis for several SinaWeibo IDTs and Twitter IDTs indicate that the IDT null models presented in this paper perform well in terms of degree correlation of nodes and dissemination path cascade ratio, which can be better to reveal the features of information dissemination and to fit the situation of real social networks.

Suggested Citation

  • Zhang, Zhiwei & Wang, Zhenyu, 2017. "The data-driven null models for information dissemination tree in social networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 484(C), pages 394-411.
    • Handle: RePEc:eee:phsmap:v:484:y:2017:i:c:p:394-411
    DOI: 10.1016/j.physa.2017.05.008
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378437117304995
    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.2017.05.008?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. Yi, Chengqi & Bao, Yuanyuan & Xue, Yibo, 2016. "Mining the key predictors for event outbreaks in social networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 447(C), pages 247-260.
    2. Maslov, Sergei & Sneppen, Kim & Zaliznyak, Alexei, 2004. "Detection of topological patterns in complex networks: correlation profile of the internet," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 333(C), pages 529-540.
    3. Zhang, Zhiwei & Wang, Zhenyu, 2015. "Mining overlapping and hierarchical communities in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 421(C), pages 25-33.
    Full references (including those not matched with items on IDEAS)

    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. Christoph Schmidt & Thomas Weiss & Thomas Lehmann & Herbert Witte & Lutz Leistritz, 2013. "Extracting Labeled Topological Patterns from Samples of Networks," PLOS ONE, Public Library of Science, vol. 8(8), pages 1-11, August.
    2. Yin, Liang & Shi, Li-Chen & Zhao, Jun-Yan & Du, Song-Yang & Xie, Wen-Bo & Yuan, Fei & Chen, Duan-Bing, 2018. "Heterogeneous information network model for equipment-standard system," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 490(C), pages 935-943.
    3. Giorgio Fagiolo & Tiziano Squartini & Diego Garlaschelli, 2013. "Null models of economic networks: the case of the world trade web," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 8(1), pages 75-107, April.
    4. Zhou, Kuang & Martin, Arnaud & Pan, Quan, 2015. "A similarity-based community detection method with multiple prototype representation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 438(C), pages 519-531.
    5. Dhal, R. & Abad Torres, J. & Roy, S., 2015. "Detecting link failures in complex network processes using remote monitoring," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 437(C), pages 36-54.
    6. Wen, Shaobo & An, Haizhong & Chen, Zhihua & Liu, Xueyong, 2017. "Driving factors of interactions between the exchange rate market and the commodity market: A wavelet-based complex network perspective," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 479(C), pages 299-308.
    7. Luu, Duc Thi & Lux, Thomas & Yanovski, Boyan, 2017. "Structural correlations in the Italian overnight money market: An analysis based on network configuration models," Economics Working Papers 2017-02, Christian-Albrechts-University of Kiel, Department of Economics.
    8. Bokwon Lee & Kyu-Min Lee & Jae-Suk Yang, 2019. "Network structure reveals patterns of legal complexity in human society: The case of the Constitutional legal network," PLOS ONE, Public Library of Science, vol. 14(1), pages 1-15, January.
    9. Chen, Zigang & Li, Lixiang & Peng, Haipeng & Liu, Yuhong & Yang, Yixian, 2018. "Attributed community mining using joint general non-negative matrix factorization with graph Laplacian," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 495(C), pages 324-335.
    10. Stephan Bialonski & Martin Wendler & Klaus Lehnertz, 2011. "Unraveling Spurious Properties of Interaction Networks with Tailored Random Networks," PLOS ONE, Public Library of Science, vol. 6(8), pages 1-13, August.
    11. Yourui Huang & Zhenping Chen & Tao Han & Xiaotao Liu, 2018. "One energy-efficient random-walk topology evolution method for underground wireless sensor networks," International Journal of Distributed Sensor Networks, , vol. 14(9), pages 15501477188, September.
    12. Sun, Ling & Liu, Yun & Bartolacci, Michael R. & Ting, I-Hsien, 2016. "A multi information dissemination model considering the interference of derivative information," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 451(C), pages 541-548.
    13. Liming Zhang & Ming Cai & Yingxin Zhang & Shuai Wang & Yao Xiao, 2024. "Two-layer network evolutionary game model applied to complex systems," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 97(11), pages 1-17, November.
    14. Huang, Zhenhua & Wu, Junxian & Zhu, Wentao & Wang, Zhenyu & Mehrotra, Sharad & Zhao, Yangyang, 2021. "Visualizing complex networks by leveraging community structures," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 565(C).
    15. Nian, Fuzhong & Liu, Weilong, 2016. "Hybrid synchronization of heterogeneous chaotic systems on dynamic network," Chaos, Solitons & Fractals, Elsevier, vol. 91(C), pages 554-561.
    16. Xiao, Yu & Han, Jingti, 2016. "Forecasting new product diffusion with agent-based models," Technological Forecasting and Social Change, Elsevier, vol. 105(C), pages 167-178.
    17. Huang, Xuan & An, Haizhong & Gao, Xiangyun & Hao, Xiaoqing & Liu, Pengpeng, 2015. "Multiresolution transmission of the correlation modes between bivariate time series based on complex network theory," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 428(C), pages 493-506.
    18. Pigorsch, U. & Sabek, M., 2022. "Assortative mixing in weighted directed networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 604(C).
    19. Wang, Fei & Yang, Yongqing, 2018. "Quasi-synchronization for fractional-order delayed dynamical networks with heterogeneous nodes," Applied Mathematics and Computation, Elsevier, vol. 339(C), pages 1-14.
    20. 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.

    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:484:y:2017:i:c:p:394-411. 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.