Hashkat: large-scale simulations of online social networks

From National Research Council Canada

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DOIResolve DOI: https://doi.org/10.1007/s13278-017-0424-7
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Name affiliation
  1. National Research Council of Canada. Security and Disruptive Technologies
FormatText, Article
Journal titleSocial Network Analysis and Mining
ISSN1869-5450
1869-5469
Volume7
Issue1
Subjectagent-based modeling; Kinetic Monte Carlo; online social network; network evolution; information propagation; simulation
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.
Publication date
PublisherSpringer
LanguageEnglish
Peer reviewedYes
NPARC number23003097
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Record identifier82369085-b48d-4d51-a2f7-6bb31f35b35e
Record created2018-04-19
Record modified2020-06-03
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