You may save the results dataset (as an RDS file) using the 'SAVE DATASET' button for later use or sharing with a colleague. This would save you from redoing the analysis and would speed up your research!


Integrated Value of Influence: An Integrative Method for the Identification of the Most Influential Nodes within Networks

Please cite the corresponding paper of the IVI and put a link to the Influential Software Package web portal if you used this shiny app in your study. The IVI is published in Patterns, a gold standard data science journal published by the Cell Press.

Salavaty A, Ramialison M, Currie PD. Integrated Value of Influence: An Integrative Method for the Identification of the Most Influential Nodes within Networks. Patterns (N Y). 2020 Jun 22;1(5):100052.

  • DOI: 10.1016/j.patter.2020.100052
  • PMID: 33205118
  • PMCID: PMC7660386
  • The Bigger Picture

    Decoding the information buried within the interconnection of components could have several benefits for the smart control of a complex system. One of the major challenges in this regard is the identification of the most influential individuals that have the potential to cause the highest impact on the entire network. This knowledge could provide the ability to increase network efficiency and reduce costs. In this article, we present a novel algorithm termed the Integrated Value of Influence (IVI) that combines the most important topological characteristics of the network to identify the key individuals within it. The IVI is a versatile method that could benefit several fields such as sociology, economics, transportation, biology, and medicine. In biomedical research, for instance, identification of the true influential nodes within a disease-associated network could lead to the discovery of novel biomarkers and/or drug targets, a process that could have a considerable impact on society.

    R package influential

    The IVI function as well as the centrality-based visualization function are part of the R package influential Additionally, several other functions have been provided for the calculation of some commonly used centrality measures as well as the extraction, classification and ranking of top candidate features from experimental data. You may install the R package influential via either CRAN or its GitHub repo .

    • CRAN:


    • GitHub repo:

      ## install.packages('devtools')
                  build_vignettes = TRUE)

    What is IVI?

    The Integrated Value of Influence (IVI) is a robust and versatile algorithm that captures all topological dimensions of a network for the identification of network most influential nodes. To this end, IVI integrates the most significant network centrality measures in order to synergize their effects and simultaneously remove their biases. The IVI is the first method that truly integrates the effect of six important network centrality measures.


    Change Log - version and update history

    Latest update: Aug 20, 2023.

    Version 1.1

    • IVI function debugged.
    • A new method (Z-score standardization) is added to the shiny app).
    • The implementations of shiny app is optimized.

    Update: Aug 20, 2023

    Version 1.0

    Latest update: April 29, 2021.


    The IVI project was done by Adrian (Abbas) Salavaty and was supervised by Prof. Peter Currie and Assoc. Prof. Mirana Ramialison . The IVI shiny app was designed and developed by Adrian according to the IVI function of the R package influential . Also, the visualization of the networks based on IVI values has been rooted from another function of the influential R package . You may have a look at the CITATION tab to get more information regarding the influential R package and how to install it.

    To get more information about the Influential Software Package team refer to the About page of the Influential Software Package portal .

    Also, there is a Youtube channel dedicated to tutorial videos of different functions of the influential R package.


    • We would like to thanks Ehsan Rezaei-Darzi (MSc Biostatistics) for his consultations regarding proper use of statistical methods and evaluations in the IVI project. Also, thanks to the constructive feedback on the IVI manuscript by Hieu Tri Nim.

    • A part of the results of the IVI project is based on data generated by the TCGA Research Network.

    • The IVI project was supported by Monash University and The Australian Regenerative Medicine Institute (itself supported by grants from the State Government of Victoria and the Australian Government).


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