IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v390y2011i20p3602-3607.html
   My bibliography  Save this article

Understanding how both the partitions of a bipartite network affect its one-mode projection

Author

Listed:
  • Mukherjee, Animesh
  • Choudhury, Monojit
  • Ganguly, Niloy

Abstract

It is a well-known fact that the degree distribution (DD) of the nodes in a partition of a bipartite network influences the DD of its one-mode projection on that partition. However, there are no studies exploring the effect of the DD of the other partition on the one-mode projection. In this article, we show that the DD of the other partition, in fact, has a very strong influence on the DD of the one-mode projection. We establish this fact by deriving the exact or approximate closed-forms of the DD of the one-mode projection through the application of generating function formalism followed by the method of iterative convolution. The results are cross-validated through appropriate simulations.

Suggested Citation

  • Mukherjee, Animesh & Choudhury, Monojit & Ganguly, Niloy, 2011. "Understanding how both the partitions of a bipartite network affect its one-mode projection," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(20), pages 3602-3607.
  • Handle: RePEc:eee:phsmap:v:390:y:2011:i:20:p:3602-3607
    DOI: 10.1016/j.physa.2011.05.007
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378437111003657
    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.2011.05.007?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. Dorogovtsev, S.N. & Mendes, J.F.F., 2003. "Evolution of Networks: From Biological Nets to the Internet and WWW," OUP Catalogue, Oxford University Press, number 9780198515906.
    2. Guillaume, Jean-Loup & Latapy, Matthieu, 2006. "Bipartite graphs as models of complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 371(2), pages 795-813.
    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. Wang, Xingyuan & Qin, Xiaomeng, 2016. "Asymmetric intimacy and algorithm for detecting communities in bipartite networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 462(C), pages 569-578.
    2. Arthur, Rudy, 2020. "Modularity and projection of bipartite networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 549(C).
    3. Biggiero, Lucio & Angelini, Pier Paolo, 2015. "Hunting scale-free properties in R&D collaboration networks: Self-organization, power-law and policy issues in the European aerospace research area," Technological Forecasting and Social Change, Elsevier, vol. 94(C), pages 21-43.
    4. Sun, Hong-liang & Ch’ng, Eugene & Yong, Xi & Garibaldi, Jonathan M. & See, Simon & Chen, Duan-bing, 2018. "A fast community detection method in bipartite networks by distance dynamics," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 496(C), pages 108-120.
    5. Feng, Liang & Zhou, Cangqi & Zhao, Qianchuan, 2019. "A spectral method to find communities in bipartite networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 513(C), pages 424-437.

    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. Ya-Chun Gao & Zong-Wen Wei & Bing-Hong Wang, 2013. "Dynamic Evolution Of Financial Network And Its Relation To Economic Crises," International Journal of Modern Physics C (IJMPC), World Scientific Publishing Co. Pte. Ltd., vol. 24(02), pages 1-10.
    2. Zhou, Wei-Xing & Jiang, Zhi-Qiang & Sornette, Didier, 2007. "Exploring self-similarity of complex cellular networks: The edge-covering method with simulated annealing and log-periodic sampling," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 375(2), pages 741-752.
    3. Bezsudnov, I.V. & Snarskii, A.A., 2014. "From the time series to the complex networks: The parametric natural visibility graph," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 414(C), pages 53-60.
    4. Mark S. Handcock & Adrian E. Raftery & Jeremy M. Tantrum, 2007. "Model‐based clustering for social networks," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 170(2), pages 301-354, March.
    5. Wang, Qingyun & Duan, Zhisheng & Chen, Guanrong & Feng, Zhaosheng, 2008. "Synchronization in a class of weighted complex networks with coupling delays," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(22), pages 5616-5622.
    6. Jorge Peña & Yannick Rochat, 2012. "Bipartite Graphs as Models of Population Structures in Evolutionary Multiplayer Games," PLOS ONE, Public Library of Science, vol. 7(9), pages 1-13, September.
    7. F. W. S. Lima, 2015. "Evolution of egoism on semi-directed and undirected Barabási-Albert networks," International Journal of Modern Physics C (IJMPC), World Scientific Publishing Co. Pte. Ltd., vol. 26(12), pages 1-9.
    8. G. Ghoshal & M. E.J. Newman, 2007. "Growing distributed networks with arbitrary degree distributions," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 58(2), pages 175-184, July.
    9. Chang, Y.F. & Han, S.K. & Wang, X.D., 2018. "The way to uncover community structure with core and diversity," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 501(C), pages 111-119.
    10. Chakrabarti, Anindya S., 2015. "Stochastic Lotka-Volterra equations: A model of lagged diffusion of technology in an interconnected world," IIMA Working Papers WP2015-08-05, Indian Institute of Management Ahmedabad, Research and Publication Department.
    11. Roth, Camille, 2007. "Empiricism for descriptive social network models," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 378(1), pages 53-58.
    12. Douglas R. White & Jason Owen-Smith & James Moody & Walter W. Powell, 2004. "Networks, Fields and Organizations: Micro-Dynamics, Scale and Cohesive Embeddings," Computational and Mathematical Organization Theory, Springer, vol. 10(1), pages 95-117, May.
    13. L. da F. Costa & L. E.C. da Rocha, 2006. "A generalized approach to complex networks," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 50(1), pages 237-242, March.
    14. Perc, Matjaž, 2010. "Zipf’s law and log-normal distributions in measures of scientific output across fields and institutions: 40 years of Slovenia’s research as an example," Journal of Informetrics, Elsevier, vol. 4(3), pages 358-364.
    15. Florian Blöchl & Fabian J. Theis & Fernando Vega-Redondo & Eric O'N. Fisher, 2010. "Which Sectors of a Modern Economy are most Central?," CESifo Working Paper Series 3175, CESifo.
    16. Rita Strohmaier & Marlies Schuetz & Simone Vannuccini, 2019. "A systemic perspective on socioeconomic transformation in the digital age," Economia e Politica Industriale: Journal of Industrial and Business Economics, Springer;Associazione Amici di Economia e Politica Industriale, vol. 46(3), pages 361-378, September.
    17. M. C. González & A. O. Sousa & H. J. Herrmann, 2004. "Opinion Formation On A Deterministic Pseudo-Fractal Network," International Journal of Modern Physics C (IJMPC), World Scientific Publishing Co. Pte. Ltd., vol. 15(01), pages 45-57.
    18. A. Chatterjee, 2009. "Kinetic models for wealth exchange on directed networks," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 67(4), pages 593-598, February.
    19. Z.-Q. Jiang & L. Guo & W.-X. Zhou, 2007. "Endogenous and exogenous dynamics in the fluctuations of capital fluxes," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 57(3), pages 347-355, June.
    20. D Dylan Johnson Restrepo & Neil F Johnson, 2017. "Unraveling the Collective Dynamics of Complex Adaptive Biomedical Systems," Current Trends in Biomedical Engineering & Biosciences, Juniper Publishers Inc., vol. 8(5), pages 118-132, September.

    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:390:y:2011:i:20:p:3602-3607. 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.