A Kuramoto Model Based Approach to Extract and Assess Influence Relations

Marcello Trovati, Aniello Castiglione, Nik Bessis, Richard Hill

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Citations (Scopus)

Abstract

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.

Original languageEnglish
Title of host publicationComputational Intelligence and Intelligent Systems
Subtitle of host publication7th International Symposium, ISICA 2015, Guangzhou, China, November 21-22, 2015, Revised Selected Papers
EditorsKangshun Li, Jin Li, Yong Liu, Aniello Castiglione
PublisherSpringer Verlag
Pages464-473
Number of pages10
ISBN (Electronic)9789811003561
ISBN (Print)9789811003554
DOIs
Publication statusPublished - 19 Jan 2016
Externally publishedYes
Event7th International Symposium on Computational Intelligence and Intelligent Systems - Guangzhou, China
Duration: 21 Nov 201522 Nov 2015
Conference number: 7

Publication series

NameCommunications in Computer and Information Science
PublisherSpringer
Volume575
ISSN (Print)1865-0929
ISSN (Electronic)1865-0937

Conference

Conference7th International Symposium on Computational Intelligence and Intelligent Systems
Abbreviated titleISICA 2015
CountryChina
CityGuangzhou
Period21/11/1522/11/15

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  • Cite this

    Trovati, M., Castiglione, A., Bessis, N., & Hill, R. (2016). A Kuramoto Model Based Approach to Extract and Assess Influence Relations. In K. Li, J. Li, Y. Liu, & A. Castiglione (Eds.), Computational Intelligence and Intelligent Systems: 7th International Symposium, ISICA 2015, Guangzhou, China, November 21-22, 2015, Revised Selected Papers (pp. 464-473). (Communications in Computer and Information Science; Vol. 575). Springer Verlag. https://doi.org/10.1007/978-981-10-0356-1_49