Studying Social Networks at Scale: Macroscopic Anatomy of the Twitter Social Graph

Abstract : Twitter is one of the largest social networks using exclusively directed links among accounts. This makes the Twitter social graph much closer to the social graph supporting real life communications than, for instance, Facebook. Therefore, understanding the structure of the Twitter social graph is interesting not only for computer scientists, but also for researchers in other fields, such as sociologists. However, little is known about how the information propagation in Twitter is constrained by its inner structure. In this paper, we present an in-depth study of the macroscopic structure of the Twitter social graph unveiling the highways on which tweets propagate, the specific user activity associated with each component of this macroscopic structure, and the evolution of this macroscopic structure with time for the past 6 years. For this study, we crawled Twitter to retrieve all accounts and all social relationships (follow links) among accounts; the crawl completed in July 2012 with 505 million accounts interconnected by 23 billion links. Then, we present a methodology to unveil the macroscopic structure of the Twitter social graph. This macroscopic structure consists of 8 components defined by their connectivity characteristics. Each component group users with a specific usage of Twitter. For instance, we identified components gathering together spammers, or celebrities. Finally, we present a method to approximate the macroscopic structure of the Twitter social graph in the past, validate this method using old datasets, and discuss the evolution of the macroscopic structure of the Twitter social graph during the past 6 years.
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ACM Sigmetrics 2014, Jun 2014, Austin, United States. 2014
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  • HAL Id : hal-00948889, version 1
  • ARXIV : 1404.1355

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Maksym Gabielkov, Ashwin Rao, Arnaud Legout. Studying Social Networks at Scale: Macroscopic Anatomy of the Twitter Social Graph. ACM Sigmetrics 2014, Jun 2014, Austin, United States. 2014. 〈hal-00948889〉

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