Personalized Social Connectivity and Reputation

Monitoring Dynamics in Online Networks with Aigents Platform
  • Anton KoloninEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 886)


The paper describes the approach and solution for personalized assessment of social interaction patterns in online social networks. The approach and the solution are used for temporal monitoring and study of social communication dynamics, as well as for the personal reputation management.


Big data Communication pattern Personalization Social network 


  1. 1.
    Kolonin, A.: Intelligent agent for web watching: belief system and architecture. In: Knowledge-Ontology-Theories (KONT-2015) Conference Proceedings, Novosibirsk, Russia, vol. 1, pp. 140–1491 (2015)Google Scholar
  2. 2.
    Haikonen, P.: Reflections of consciousness; the mirror test. In: AAAI Symposium, Washington, D.C. (2007).
  3. 3.
    Takeno, J.: A robot succeeds in 100% mirror image cognition. Int. J. Smart Sens. Intell. Syst. 1(4), 891–911 (2008)Google Scholar
  4. 4.
    Kolonin, A.: Studying human social environment and state with social network data. In: Cognitive Sciences, Genomics and Bioinformatics (CSGB) - Symposium Proceedings (2016).
  5. 5.
    Doctorow, C.: Down and Out in the Magic Kingdom. Tor Books, US (2003). ISBN 0-7653-0436-8Google Scholar
  6. 6.
    Farmer, F., Glass, B.: Building Web Reputation Systems. O’Reilly, Yahoo Press, Sebastopol (2010)Google Scholar
  7. 7.
    Chin, J., Wong, G.: China’s new tool for social control: a credit rating for everything. Wall Str. J. (2016). ISSN 0099-9660Google Scholar
  8. 8.
    Goertzel, B.: CogPrime: An Integrative Architecture for Embodied Artificial General Intelligence. Open Cog (2012).
  9. 9.
    Kolonin, A.: Computable cognitive model based on social evidence and restricted by resources: applications for personalized search and social media in multi-agent environments. In: International Conference on Biomedical Engineering and Computational Technologies (SIBIRCON), Novosibirsk, Russia (2015).
  10. 10.
    Kolonin, A., Vityaev, E., Orlov, Y.: Cognitive architecture of collective intelligence based on social evidence. In: Proceedings of 7th Annual International Conference on Biologically Inspired Cognitive Architectures, BICA 2016, NY, USA, July 2016.
  11. 11.
    Cialdini, R.: Influence: The Psychology of Persuasion (1984). ISBN 0-688-12816-5Google Scholar
  12. 12.
    Kramer, A., Guillory, J., Hancock, J.: Experimental evidence of massive-scale emotional contagion through social networks. PNAS 111(24), 8788–8790 (2014)CrossRefGoogle Scholar
  13. 13.
    Dhand, A., Luke, D., Lang, C., Lee, J.: Social networks and neurological illness. Nat. Rev. Neurol. 12(10), 605–612 (2016). Epub 2016CrossRefGoogle Scholar
  14. 14.
    Kolonin, A.: Automatic text classification and property extraction. In: SIBIRCON/SibMedInfo Conference Proceedings, pp. 27–31 (2015). ISBN 987-1-4673-9109-2Google Scholar
  15. 15.
    Vityaev, E.: Unified formalization of «natural» classification, «natural» concepts, and consciousness as integrated information by Giulio Tononi. In: The Sixth International Conference on Biologically Inspired Cognitive Architectures, BICA 2015, 6–8 November 2015, Lyon, France. Elsevier. Procedia Comput. Sci. 71, 169–177 (2015)Google Scholar
  16. 16.
    Kolonin, A.: Assessment of personal environments in social networks. In: 2017 Siberian Symposium on Data Science and Engineering (SSDSE), Novosibirsk, pp. 61–64 (2017). ISBN 978-1-5386-1592-8.

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Aigents GroupNovosibirskRussia

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