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A time evolving online social network generation algorithm.

Pouyan Shirzadian1, Blessy Antony2, Akshaykumar G Gattani3

  • 1Department of Computer Science, Virginia Tech, Blacksburg, VA, 24061, US. pshirzadian@vt.edu.

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Summary
This summary is machine-generated.

We developed EpiCNet, a novel algorithm to generate realistic, time-evolving online social networks. This method aids in studying social network dynamics when real data is inaccessible.

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Area of Science:

  • Network Science
  • Computational Social Science
  • Epidemiology

Background:

  • Online social media usage is rapidly increasing, highlighting the need to analyze online social network dynamics.
  • Dynamic data from existing social media platforms is often inaccessible for research.
  • There is a necessity to synthesize networks that emulate real-world online social media for further study.

Purpose of the Study:

  • To propose an algorithm, EpiCNet, for generating time-evolving online social networks.
  • To create synthetic networks that closely mirror the characteristics of real-world social networks.
  • To provide a tool for studying social network dynamics when real data is unavailable.

Main Methods:

  • Developed an epidemiology-inspired and community-based algorithm (EpiCNet).
  • Utilized compartmental models from mathematical epidemiology to simulate user flow.
  • Incorporated overlapping community structures and time-evolving network properties based on individual behavior.

Main Results:

  • EpiCNet generates both undirected and directed networks.
  • The algorithm successfully emulates real-world network properties like clustering coefficient, node degree, and diameter.
  • Synthetic networks closely match properties of real-world networks such as Facebook and Twitter.

Conclusions:

  • EpiCNet is a versatile algorithm for generating realistic, time-evolving online social networks.
  • The tunable parameters allow simulation of diverse network behaviors and community evolution.
  • This approach provides a valuable method for analyzing social network dynamics.