Utilizing the average node degree to assess the temporal growth rate of Twitter

  • Despoina Antonakaki
  • Sotiris Ioannidis
  • Paraskevi Fragopoulou
Original Article


Several models have been proposed that describe the evolution of the graph properties of many online social networks (OSNs) and explain the behavior of their users. These models are essential for understanding the growth dynamics of the underlying social graph. One of the most prominent OSNs is Twitter, since it covers a significant part of the online worldwide population. Nevertheless, investigating the validity of these models on Twitter entails many difficulties. The size of Twitter and the limitations of its access API make extremely difficult the estimation of many graph properties and therefore the evaluation of the proposed models. In this study, we present a simple and efficient method to fit an already existing model, which describes the densification power law property of modern OSNs. This model states that the average degree of an OSN increases over time. In a case study, we assess this model in two large samples of Twitter, and we demonstrate how it can portray the altering growth periods of Twitter. Finally, we make some remarks on several events during the early period of Twitter that may have affected its growth rates.


Twitter Evolution Average node degree Temporal growth rate Online social networks Densification power law 



We would like to thank the anonymous reviewers that provided valuable comments and feedback. We are also grateful to prof. Marian Boguna and Kolja Kleineberg for the discussions and the contribution on the infrastructure at the University of Barcelona. Also we would like to thank Hariton Efstathiades and Demetris Antoniades for their valuable comments as well as the University of Cyprus on the valuable contribution of their infrastructure in order to complete the experiments. This work was supported by the following research projects: FP7 Marie-Curie ITN iSocial funded by the EC under Grant Agreement No. 316808, UNICORN: Funded by the European Commission (H2020-ICT-2016-1/ICT-06-2016) and EUNITY: Funded by the European Commission (H2020-DS-2016-2017/DS-05-2016).


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Copyright information

© Springer-Verlag GmbH Austria, part of Springer Nature 2018

Authors and Affiliations

  • Despoina Antonakaki
    • 1
  • Sotiris Ioannidis
    • 1
  • Paraskevi Fragopoulou
    • 1
  1. 1.FORTH-ICSHeraklionGreece

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