2014/11/29 by Kent Miyajima, Miyajima, Kent, Takashi Sakuragawa +1
Biochemistry, Genetics and Molecular Biology · Computer Science · Physics and Astronomy · #Advanced Graph Neural Networks #Bioinformatics and Genomic Networks #Complex Network Analysis Techniques #FOS: Computer and information sciences #FOS: Physical sciences #Physics and Society (physics.soc-ph) #Social and Information Networks (cs.SI) #cs.SI #physics.soc-ph
paper · pdf · doi:10.48550/arxiv.1412.0059
29 pages, 14 figures
arxiv created 2014/11/29 · openalex publication_date 2014/11/29 · arxiv updated 2014/12/02 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
We introduce new clustering coefficients for weighted networks. They are continuous and robust against edge weight changes. Recently, generalized clustering coefficients for weighted and directed networks have been proposed. These generalizations have a common property, that their values are not continuous. They are sensitive with edge weight changes, especially at zero weight. With these generalizations, if vanishingly low weights of edges are truncated to weight zero for some reason, the coefficient value may change significantly from the original value. It is preferable that small changes of edge weights cause small changes of coefficient value. We call this property the continuity of generalized clustering coefficients. Our new coefficients admit this property. In the past, few studies have focused on the continuity of generalized clustering coefficients. In experiments, we performed comparative assessments of existing and our generalizations. In the case of a real world network dataset (C. Elegans Neural network), after adding random edge weight errors, though the value of one discontinuous generalization was changed about 436%, the value of proposed one was only changed 0.2%.