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Directed closure coefficient and its patterns.
Jia, Mingshan; Gabrys, Bogdan; Musial, Katarzyna.
Afiliação
  • Jia M; School of Computer Science, University of Technology Sydney, Sydney, NSW, Australia.
  • Gabrys B; School of Computer Science, University of Technology Sydney, Sydney, NSW, Australia.
  • Musial K; School of Computer Science, University of Technology Sydney, Sydney, NSW, Australia.
PLoS One ; 16(6): e0253822, 2021.
Article em En | MEDLINE | ID: mdl-34170971
ABSTRACT
The triangle structure, being a fundamental and significant element, underlies many theories and techniques in studying complex networks. The formation of triangles is typically measured by the clustering coefficient, in which the focal node is the centre-node in an open triad. In contrast, the recently proposed closure coefficient measures triangle formation from an end-node perspective and has been proven to be a useful feature in network analysis. Here, we extend it by proposing the directed closure coefficient that measures the formation of directed triangles. By distinguishing the direction of the closing edge in building triangles, we further introduce the source closure coefficient and the target closure coefficient. Then, by categorising particular types of directed triangles (e.g., head-of-path), we propose four closure patterns. Through multiple experiments on 24 directed networks from six domains, we demonstrate that at network-level, the four closure patterns are distinctive features in classifying network types, while at node-level, adding the source and target closure coefficients leads to significant improvement in link prediction task in most types of directed networks.
Assuntos

Texto completo: 1 Bases de dados: MEDLINE Assunto principal: Algoritmos / Modelos Teóricos Tipo de estudo: Prognostic_studies Idioma: En Revista: PLoS One Assunto da revista: CIENCIA / MEDICINA Ano de publicação: 2021 Tipo de documento: Article País de afiliação: Austrália

Texto completo: 1 Bases de dados: MEDLINE Assunto principal: Algoritmos / Modelos Teóricos Tipo de estudo: Prognostic_studies Idioma: En Revista: PLoS One Assunto da revista: CIENCIA / MEDICINA Ano de publicação: 2021 Tipo de documento: Article País de afiliação: Austrália