Hashkat: large-scale simulations of online social networks

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• 作者：Kevin Ryczko ; Adam Domurad ; Nicholas Buhagiar…
• 关键词：Agent ; based modeling ; Kinetic Monte Carlo ; Online social network ; Network evolution ; Information propagation ; Simulation
• 刊名：Social Network Analysis and Mining
• 出版年：2017
• 出版时间：December 2017
• 年：2017
• 卷：7
• 期：1
• 全文大小：
• 刊物类别：Computer Science
• 刊物主题：Data Mining and Knowledge Discovery; Applications of Graph Theory and Complex Networks; Game Theory, Economics, Social and Behav. Sciences; Statistics for Social Science, Behavorial Science, Education
• 出版者：Springer Vienna
• ISSN：1869-5469
• 卷排序：7

文摘

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.