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Measuring the complexity of directed graphs: A polynomial-based approach.
Dehmer, Matthias; Chen, Zengqiang; Emmert-Streib, Frank; Tripathi, Shailesh; Mowshowitz, Abbe; Levitchi, Alexei; Feng, Lihua; Shi, Yongtang; Tao, Jin.
Afiliación
  • Dehmer M; Institute for Intelligent Production, Faculty for Management, University of Applied Sciences Upper Austria, Steyr, Austria.
  • Chen Z; College of Artificial Intelligence, Nankai University, Tianjin, China.
  • Emmert-Streib F; Department of Biomedical Computer Science and Mechatronics, UMIT - The Health and Lifesciences University, A-6060 Hall in Tyrol, Austria.
  • Tripathi S; College of Artificial Intelligence, Nankai University, Tianjin, China.
  • Mowshowitz A; Predictive Medicine and Data Analytics Lab, Department of Signal Processing, Tampere University, Tampere, Finland.
  • Levitchi A; Institute of Biosciences and Medical Technology, Tampere, Finland.
  • Feng L; Institute for Intelligent Production, Faculty for Management, University of Applied Sciences Upper Austria, Steyr, Austria.
  • Shi Y; Department of Computer Science, The City College of New York (CUNY), 138th Street at Convent Avenue, New York, United States of America.
  • Tao J; Institute for Intelligent Production, Faculty for Management, University of Applied Sciences Upper Austria, Steyr, Austria.
PLoS One ; 14(11): e0223745, 2019.
Article en En | MEDLINE | ID: mdl-31725742
In this paper, we define novel graph measures for directed networks. The measures are based on graph polynomials utilizing the out- and in-degrees of directed graphs. Based on these polynomial, we define another polynomial and use their positive zeros as graph measures. The measures have meaningful properties that we investigate based on analytical and numerical results. As the computational complexity to compute the measures is polynomial, our approach is efficient and can be applied to large networks. We emphasize that our approach clearly complements the literature in this field as, to the best of our knowledge, existing complexity measures for directed graphs have never been applied on a large scale.
Asunto(s)

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Gráficos por Computador / Biología Computacional Idioma: En Revista: PLoS One Asunto de la revista: CIENCIA / MEDICINA Año: 2019 Tipo del documento: Article País de afiliación: Austria

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Gráficos por Computador / Biología Computacional Idioma: En Revista: PLoS One Asunto de la revista: CIENCIA / MEDICINA Año: 2019 Tipo del documento: Article País de afiliación: Austria
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