A Kuramoto Model Based Approach to Extract and Assess Influence Relations

  • Marcello TrovatiEmail author
  • Aniello Castiglione
  • Nik Bessis
  • Richard Hill
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 575)


In this paper, we introduce a novel method to extract and assess influence relations between concepts, based on a variation of the Kuramoto Model. The initial evaluation focusing on an unstructured dataset provided by the abstracts and articles freely available from PubMed [7], shows the potential of our approach, as well as suggesting its applicability to a wide selection of multidisciplinary topics.


Knowledge discovery Information extraction Data analytics 


  1. 1.
    Trovati, M., Bessis, N., Huber, A., Zelenkauskaite, A., Asimakopoulou, E.: Extraction, Identification and Ranking of Network Structures from Data Sets. In: Proceedings of CISIS, pp. 331–337 (2014)Google Scholar
  2. 2.
    Trovati, M.: Reduced topologically real-world networks: a big-data approach. Int. J. Distrib. Syst. Technol. 6(2), 13–27 (2015)CrossRefGoogle Scholar
  3. 3.
    Trovati, M.: An influence assessment method based on co-occurrence for topologically reduced big datasets. Soft Comput. 1–10 (2015). Springer, Berlin, HeidelbergGoogle Scholar
  4. 4.
    Trovati, M., Bessis, N., Palmieri, F., Hill, R.: Dynamical extraction and assessment of probabilistic information between concepts from unstructured large data sets. Submitted to IEEE transactions (2015)Google Scholar
  5. 5.
    Kuramoto, Y.: International Symposium on Mathematical Problems in Theoretical Physics. Lecture Notes in Physics, vol. 39. Springer, New York (1975)CrossRefGoogle Scholar
  6. 6.
    Trovati, J., Trovati, P., Larcombe, L., Liu, A.: Semi-automated assessment of the direction of influence relations from semantic networks: a case study in maths anxiety. In: The Proceedings of IBDS (2015)Google Scholar
  7. 7.
    PubMed Website. Accessed September 2015
  8. 8.
    Natural Language Toolkit Website. Accessed September 2015
  9. 9.
    Miller, G.: WordNet: a lexical database for English. Commun. ACM 38(11), 39–41 (1995)CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media Singapore 2016

Authors and Affiliations

  • Marcello Trovati
    • 1
    Email author
  • Aniello Castiglione
    • 2
  • Nik Bessis
    • 1
    • 3
  • Richard Hill
    • 1
  1. 1.Department of Computing and MathematicsUniversity of DerbyDerbyUK
  2. 2.Dipartimento di InformaticaUniversita’ di SalernoSalernoItaly
  3. 3.Department of ComputingEdgehill UniversityOrmskirkUK

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