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Online social networks have emerged as useful tools to communicate or shareinformation and news on a daily basis. One of the most popular networks isTwitter, where users connect to each other via directed follower relationships.Researchers have studied Twitter follower graphs and described them withvarious topological features. Collecting Twitter data, especially crawling thefollowers of users, is a tedious and time-consuming process and the data needsto be treated carefully due to its sensitive nature, containing personal userinformation. We therefore aim at the fast generation of synthetic directedsocial network graphs with reciprocal edges and high clustering. Our proposedmethod is based on a previously developed model, but relies on lesshyperparameters and has a significantly lower runtime. Results show that themethod does not only replicate the crawled directed Twitter graphs well w.r.t.several topological features and the application of an epidemics spreadingprocess, but that it is also highly scalable which allows the fast creation ofbigger graphs that exhibit similar properties as real-world networks.

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