RnSIR: A new model of information spread in online social networks

N. Sumith, B. Annappa, Swapan Bhattacharya

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Citations (Scopus)


There is a close resemblance between the dynamism of epidemic spread and information spread. For this reason, the Susceptible-Infected-Recovered(SIR) model, rooted in epidemiology, is been used to understand the information spread in online social networks. This model is based on homogeneous mixing of population, where an individual is equally likely to be infected by others. However, the degree of sparsity in interactions among the users will invalidate the homogeneous mixing concept. For this reason, SIR model fails to map the complete scenario of information spread among the users. In this paper, to fill in the gap seen in SIR, a new model RnSIR is developed. The proposed model is able to make a clear distinction between the restrained and susceptible. To this end, the new model is applied to viral marketing to understand its authenticity. The contribution is shown by the increase in spread of information reaching as far as 50% of the susceptible population in the RnSIR model, when compared to the SIR model. Although the paper discusses the dynamism of information spread in online social networks, the proposed model can be used to understand the spread of epidemics, spread of computer virus, rumors and also analyze the role of users.

Original languageEnglish
Title of host publicationProceedings of the 2016 IEEE Region 10 Conference, TENCON 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages4
ISBN (Electronic)9781509025961
Publication statusPublished - 08-02-2017
Event2016 IEEE Region 10 Conference, TENCON 2016 - Singapore, Singapore
Duration: 22-11-201625-11-2016

Publication series

NameIEEE Region 10 Annual International Conference, Proceedings/TENCON
ISSN (Print)2159-3442
ISSN (Electronic)2159-3450


Conference2016 IEEE Region 10 Conference, TENCON 2016

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Electrical and Electronic Engineering


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