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Hashkat: large-scale simulations of online social networks

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Abstract

Hashkat (http://hashkat.org) is a free, open source, agent-based simulation software package designed to simulate large-scale online social networks (e.g., Twitter, Facebook, LinkedIn). It allows for dynamic agent generation, edge creation, and information propagation. The purpose of hashkat is to study the growth of online social networks and how information flows within them. Like real-life online social networks, hashkat incorporates user relationships, information diffusion, and trending topics. Hashkat was implemented in C++ and was designed with extensibility in mind. The software includes Bash and Python scripts for easy installation and usability. In this report, we describe all of the algorithms and features integrated into hashkat before moving on to example use cases. In general, hashkat can be used to understand the underlying topology of social networks, validate sampling methods of such networks, develop business strategy for advertising on online social networks, and test new features of an online social network before going into production.

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Acknowledgements

The authors would like to thank the National Sciences and Engineering Research Council of Canada (NSERC) for funding as well as Compute Canada for computational resources.

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Authors and Affiliations

  1. University of Ontario Institute of Technology, Oshawa, Canada

    Kevin Ryczko

  2. University of Waterloo, Waterloo, Canada

    Adam Domurad

  3. Ryerson University, Toronto, Canada

    Nicholas Buhagiar

  4. National Research Council of Canada, Ottawa, Canada

    Isaac Tamblyn

Authors
  1. Kevin Ryczko
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  2. Adam Domurad
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  3. Nicholas Buhagiar
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  4. Isaac Tamblyn
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Correspondence to Kevin Ryczko.

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Ryczko, K., Domurad, A., Buhagiar, N. et al. Hashkat: large-scale simulations of online social networks. Soc. Netw. Anal. Min. 7, 4 (2017). https://doi.org/10.1007/s13278-017-0424-7

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  • DOI: https://doi.org/10.1007/s13278-017-0424-7

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