IDEAS home Printed from https://ideas.repec.org/a/eee/ininma/v55y2020ics0268401220314286.html
   My bibliography  Save this article

Which similarity measure to use in network analysis: Impact of sample size on phi correlation coefficient and Ochiai index

Author

Listed:
  • Kalgotra, Pankush
  • Sharda, Ramesh
  • Luse, Andy

Abstract

Some networks are explicit where members make direct connections (e.g. Facebook network), whereas other networks are implicit (e.g. co-citation network) in which an edge between two nodes is inferred using a similarity index. Choosing the right index to infer connections in an implicit/inferred network is important because conclusions can be biased if a network does not represent true relationships. In this study, we compared two indexes: Phi Correlation Coefficient (PCC) and Ochiai Coefficient (Och) based on their sensitivity to the sample size of transactions from where the network is inferred. For demonstration, we used an implicit network, called a comorbidity network, developed from health records of 22.1 million patients. The networks were compared based on their overall topologies and node centralities. Results showed that the network formed using Och was more robust to the sample size than PCC. The network using Och followed a small-world topology irrespective of the sample size whereas the structure of a network using PCC was inconsistent. Regarding node centralities, the betweenness centrality was most affected by the sample size. Our results lead us to recommend Och over PCC.

Suggested Citation

  • Kalgotra, Pankush & Sharda, Ramesh & Luse, Andy, 2020. "Which similarity measure to use in network analysis: Impact of sample size on phi correlation coefficient and Ochiai index," International Journal of Information Management, Elsevier, vol. 55(C).
    • Handle: RePEc:eee:ininma:v:55:y:2020:i:c:s0268401220314286
    DOI: 10.1016/j.ijinfomgt.2020.102229
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0268401220314286
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ijinfomgt.2020.102229?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.

    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:ininma:v:55:y:2020:i:c:s0268401220314286. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: https://www.journals.elsevier.com/international-journal-of-information-management .

    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.