Hashkat: large-scale simulations of online social networks

• Published in: Social network analysis and mining Vol. 7; no. 1; p. 4
• Main Authors: Ryczko, Kevin, Domurad, Adam, Buhagiar, Nicholas, Tamblyn, Isaac
• Format: Journal Article
• Language: English
• Published: Vienna Springer 01.12.2017; Springer Vienna; Springer Nature B.V
• Subjects: Advertising; agent-based modeling; Algorithms; Applications of Graph Theory and Complex Networks; Computer Science; Data Mining and Knowledge Discovery; Economics; Game Theory; Humanities; Information dissemination; Information flow; information propagation; Installation; Kinetic Monte Carlo; Law; Methodology of the Social Sciences; network evolution; online social network; Original Article; Python; Random variables; Scripts; Simulation; Social and Behav. Sciences; Social networks; Software; Software packages; Statistics for Social Sciences; Topology; Agent-based modeling; Network evolution; Kinetic Monte Carlo; Simulation; Information propagation; Online social network
• ISSN: 1869-5469; 1869-5450; 1869-5469
• DOI: 10.1007/s13278-017-0424-7

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.
NRC publication: Yes