Advertisement

Assessing Strategies for Sampling Dynamic Social Networks

  • Paolo CeravoloEmail author
  • Francesco Ciclosi
  • Emanuele Bellini
  • Ernesto Damiani
Conference paper
Part of the Springer Proceedings in Complexity book series (SPCOM)

Abstract

Social Networks represents an invaluable source of information to detect, understand and predict social trends and complex dynamics. Unfortunately, the presence of several constraints in data collections as costs, dimensions, time and so forth, requires the implementation of a sampling strategy able to maximize the information value for the analysis. The paper defines a number of parameters and thresholds defining a new strategy for data sampling in social network and compares samples obtained with different strategies. The test case has been conducted on the social network of the mayors of four Italian metropolitan areas. Results of the assessment reveal that the parameter designed for configuring a strategy impacts on the dimension, the extraction time and the quality of the generated network as expected. The best tradeoff between quality and execution time has been identified and discussed.

References

  1. 1.
    Gartner, I.: Gartner IT Glossary > Big Data . [In linea]. Available at: https://www.gartner.com/it-glossary/big-data/. [Consultato: 28-gen-2019]
  2. 2.
    Buttarelli, G.: European data protection supervisor. In: Opinion 7/2015—Meeting the Challenges of Big Data, pp. 1–21. European Data Protection Supervisor, Brussels (2015)Google Scholar
  3. 3.
    Adedoyin-Olowe, M., Gaber, M.M. Stahl, F.: Survey of data mining techniques for social media analysis. J. Data Min. Digit. Humanit. Digit. Humanit. 2014 (2014)Google Scholar
  4. 4.
    Borgatti, S.P.: Social network analysis, two-mode concepts in. In: Computational Complexity: Theory, Techniques, and Applications, pp. 2912–2924. Springer New York, R. A. Meyers, A c. di New York, NY (2012)CrossRefGoogle Scholar
  5. 5.
    Coscia, M., Rossi, L.: Benchmarking API costs of network sampling strategies. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 663–672 (2018)Google Scholar
  6. 6.
    (Twitter Inc.).: Twitter—API access that scales with you and your solution. [In linea]. Available at: https://developer.twitter.com/en/pricing. [Consultato: 28-gen-2019]
  7. 7.
    Twitter—Rate limits. [In linea]. Available at: https://developer.twitter.com/en/docs/basics/rate-limits. [Consultato: 28-gen-2019]
  8. 8.
    Franchin, M.: Strategie di sampling di social network. Università degli Studi di Milano (2018)Google Scholar
  9. 9.
    Ceravolo, P., Guerretti, S.: Testing social network metrics for measuring electoral success in the Italian municipal campaign of 2011. In: 2013 International Conference on Cloud and Green Computing, pp. 342–347 (2013)Google Scholar
  10. 10.
    Gambino, G.: Studio sperimentale di algoritmi di social media mining applicati a Twitter. Università degli Studi di Milano (2018)Google Scholar
  11. 11.
    twitter4J. [In linea]. Available at: http://twitter4j.org/en/index.html. [Consultato: 28-gen-2019]
  12. 12.
    Martelli, F., Navigli, R.: Disambiguare le reti sociali. Gnos. - Riv. Ital. di Intell. 4, 21–27 (2018)Google Scholar
  13. 13.
    Colajanni, M.: Social: raccomandazioni per l’uso . Gnos. - Riv. Ital. di Intell. 4, 29–37 (2018)Google Scholar
  14. 14.
    Gorodnichenko, Y., Pham, T., Talavera, O.: Social media, sentiment and public opinions: evidence from #Brexit and #USElection (2018)Google Scholar
  15. 15.
    Iacus, S.M.: Social network, data science e intelligence. Gnos. - Riv. Ital. di Intell. 4, 39–45 (2018)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Sesar LabUniversità degli Studi di MilanoMilanItaly
  2. 2.Università di MacerataMacerataItaly
  3. 3.Center of Cyber-Physical SystemKhalifa UniversityAbu DhabiUAE

Personalised recommendations