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Fast generation of simple directed social network graphs with reciprocal edges and high clustering

Christoph Schweimer

Published 2022 in Social Network Analysis and Mining

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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