IDEAS home Printed from https://ideas.repec.org/a/gam/jdataj/v2y2017i4p33-d114377.html
   My bibliography  Save this article

Temporal Statistical Analysis of Degree Distributions in an Undirected Landline Phone Call Network Graph Series

Author

Listed:
  • Orgeta Gjermëni

    (Department of Mathematics, University Ismail Qemali, Str. Kosova, 9400 Vlore, Albania)

Abstract

This article aims to provide new results about the intraday degree sequence distribution considering phone call network graph evolution in time. More specifically, it tackles the following problem. Given a large amount of landline phone call data records, what is the best way to summarize the distinct number of calling partners per client per day? In order to answer this question, a series of undirected phone call network graphs is constructed based on data from a local telecommunication source in Albania. All network graphs of the series are simplified. Further, a longitudinal temporal study is made on this network graphs series related to the degree distributions. Power law and log-normal distribution fittings on the degree sequence are compared on each of the network graphs of the series. The maximum likelihood method is used to estimate the parameters of the distributions, and a Kolmogorov–Smirnov test associated with a p -value is used to define the plausible models. A direct distribution comparison is made through a Vuong test in the case that both distributions are plausible. Another goal was to describe the parameters’ distributions’ shape. A Shapiro-Wilk test is used to test the normality of the data, and measures of shape are used to define the distributions’ shape. Study findings suggested that log-normal distribution models better the intraday degree sequence data of the network graphs. It is not possible to say that the distributions of log-normal parameters are normal.

Suggested Citation

  • Orgeta Gjermëni, 2017. "Temporal Statistical Analysis of Degree Distributions in an Undirected Landline Phone Call Network Graph Series," Data, MDPI, vol. 2(4), pages 1-10, October.
    • Handle: RePEc:gam:jdataj:v:2:y:2017:i:4:p:33-:d:114377
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2306-5729/2/4/33/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2306-5729/2/4/33/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Vuong, Quang H, 1989. "Likelihood Ratio Tests for Model Selection and Non-nested Hypotheses," Econometrica, Econometric Society, vol. 57(2), pages 307-333, March.
    2. J. P. Royston, 1982. "The W Test for Normality," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 31(2), pages 176-180, June.
    3. Gillespie, Colin S., 2015. "Fitting Heavy Tailed Distributions: The poweRlaw Package," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 64(i02).
    4. J. P. Royston, 1982. "An Extension of Shapiro and Wilk's W Test for Normality to Large Samples," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 31(2), pages 115-124, June.
    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. Lauren Bin Dong & David E. A. Giles, 2004. "An Empirical Likelihood Ratio Test for Normality," Econometrics Working Papers 0401, Department of Economics, University of Victoria.
    2. Katahira, Kei & Chen, Yu & Akiyama, Eizo, 2021. "Self-organized Speculation Game for the spontaneous emergence of financial stylized facts," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 582(C).
    3. Tomasz Górecki & Lajos Horváth & Piotr Kokoszka, 2020. "Tests of Normality of Functional Data," International Statistical Review, International Statistical Institute, vol. 88(3), pages 677-697, December.
    4. Kei Katahira & Yu Chen, 2019. "Heterogeneous wealth distribution, round-trip trading and the emergence of volatility clustering in Speculation Game," Papers 1909.03185, arXiv.org.
    5. Amir Abolhassani & Gale Boyd & Majid Jaridi & Bhaskaran Gopalakrishnan & James Harner, 2023. "“Is Energy That Different from Labor?” Similarity in Determinants of Intensity for Auto Assembly Plants," Energies, MDPI, vol. 16(4), pages 1-35, February.
    6. Jurgita Arnastauskaitė & Tomas Ruzgas & Mindaugas Bražėnas, 2021. "A New Goodness of Fit Test for Multivariate Normality and Comparative Simulation Study," Mathematics, MDPI, vol. 9(23), pages 1-20, November.
    7. Ngoc Thien Le & Watit Benjapolakul, 2019. "Evaluation of Contribution of PV Array and Inverter Configurations to Rooftop PV System Energy Yield Using Machine Learning Techniques," Energies, MDPI, vol. 12(16), pages 1-13, August.
    8. Kei Katahira & Yu Chen & Gaku Hashimoto & Hiroshi Okuda, 2019. "Development of an agent-based speculation game for higher reproducibility of financial stylized facts," Papers 1902.02040, arXiv.org.
    9. Jurgen A. Doornik & Henrik Hansen, 2008. "An Omnibus Test for Univariate and Multivariate Normality," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 70(s1), pages 927-939, December.
    10. Peter Gerbrands & Brigitte Unger & Joras Ferwerda, 2022. "Bilateral responsive regulation and international tax competition: An agent‐based simulation," Regulation & Governance, John Wiley & Sons, vol. 16(3), pages 760-780, July.
    11. Keith Pilbeam & Hamish Preston, 2019. "An Empirical Investigation of the Performance of Japanese Mutual Funds: Skill or Luck?," IJFS, MDPI, vol. 7(1), pages 1-16, January.
    12. Laszlo Gadar & Janos Abonyi, 2018. "Graph configuration model based evaluation of the education-occupation match," PLOS ONE, Public Library of Science, vol. 13(3), pages 1-19, March.
    13. Thelwall, Mike, 2016. "Citation count distributions for large monodisciplinary journals," Journal of Informetrics, Elsevier, vol. 10(3), pages 863-874.
    14. Joseph Aharony & Chan†Jane Lin & Martin P. Loeb, 1993. "Initial Public Offerings, Accounting Choices, and Earnings Management," Contemporary Accounting Research, John Wiley & Sons, vol. 10(1), pages 61-81, September.
    15. Prăvălie, Remus & Sîrodoev, Igor & Patriche, Cristian & Roșca, Bogdan & Piticar, Adrian & Bandoc, Georgeta & Sfîcă, Lucian & Tişcovschi, Adrian & Dumitraşcu, Monica & Chifiriuc, Carmen & Mănoiu, Valen, 2020. "The impact of climate change on agricultural productivity in Romania. A country-scale assessment based on the relationship between climatic water balance and maize yields in recent decades," Agricultural Systems, Elsevier, vol. 179(C).
    16. Sebastian Klaudiusz Tomczak, 2021. "Ratio Selection between Six Sectors in the Visegrad Group Using Parametric and Nonparametric ANOVA," Energies, MDPI, vol. 14(21), pages 1-20, November.
    17. Minguez, Ana & Javier Sese, F., 2022. "Why do you want a relationship, anyway? Consent to receive marketing communications and donors’ willingness to engage with nonprofits," Journal of Business Research, Elsevier, vol. 148(C), pages 356-367.
    18. Luca De Benedictis & Silvia Leoni, 2020. "Gender bias in the Erasmus students network," Papers 2003.09167, arXiv.org.
    19. Montebruno, Piero & Bennett, Robert J. & van Lieshout, Carry & Smith, Harry, 2019. "A tale of two tails: Do Power Law and Lognormal models fit firm-size distributions in the mid-Victorian era?," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 523(C), pages 858-875.
    20. Thelwall, Mike, 2016. "Are there too many uncited articles? Zero inflated variants of the discretised lognormal and hooked power law distributions," Journal of Informetrics, Elsevier, vol. 10(2), pages 622-633.

    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:gam:jdataj:v:2:y:2017:i:4:p:33-:d:114377. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.