Abstract
Online social networks have emerged as useful tools to communicate or share information and news on a daily basis. One of the most popular networks is Twitter, where users connect to each other via directed follower relationships. Twitter follower graphs have been studied and described with various topological features. Collecting Twitter data, especially crawling the followers of users, is a tedious and time-consuming process and the data needs to be treated carefully due to its sensitive nature, containing personal user information. We therefore aim at the fast generation of directed social network graphs with reciprocal edges and high clustering. Our proposed method is based on a previously developed model, but relies on less hyperparameters and has a significantly lower runtime. Results show that our method does not only replicate the crawled directed Twitter graphs well regarding several topological features and the application of an epidemics spreading process, but that it is also highly scalable which allows the fast creation of bigger graphs that exhibit similar properties as real-world networks.
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Data availability
The generated and analyzed network graphs in this study are available in the GitHub repository https://github.com/Buters147/Social_Network_Graph_Generator.
Notes
For the code see https://github.com/Buters147/Social_Network_Graph_Generator.
Connecting new first degree neighbors not only with reciprocal edges, but also with directed edges lead to increased values for the CC, exceeding 0.6, which is unrealistic for social network graphs.
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Acknowledgements
This publication is part of the project “HPC and Big Data Technologies for Global Systems” (HiDALGO), which has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 824115. The Know-Center is funded within the Austrian COMET Program—Competence Centers for Excellent Technologies—under the auspices of the Austrian Federal Ministry for Climate Action, Environment, Energy, Mobility, Innovation and Technology, the Austrian Federal Ministry for Digital and Economic Affairs and by the State of Styria. COMET is managed by the Austrian Research Promotion Agency FFG. The author thanks Bernhard C. Geiger (Know-Center GmbH) for his valuable feedback.
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Schweimer, C. Fast generation of simple directed social network graphs with reciprocal edges and high clustering. Soc. Netw. Anal. Min. 12, 127 (2022). https://doi.org/10.1007/s13278-022-00963-z
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DOI: https://doi.org/10.1007/s13278-022-00963-z