Directed clustering in weighted networks: A new perspective. (February 2018) - Clemente, G.P.; Grassi, R.
Highlights: We propose a new local clustering coefficient for weighted and directed networks. The formula well captures the presence of triangles as well as their weight. Different patterns of directed triangles from a node perspective are considered. Empirical applications on different real world networks are provided. Abstract: Several definitions of clustering coefficient for weighted networks have been proposed in literature, but less attention has been paid to both weighted and directed networks. We provide a new local clustering coefficient for this kind of networks, starting from those already existing in the literature for the weighted and undirected case. Furthermore, we extract from our coefficient four specific components, in order to separately consider different link patterns of triangles. Empirical applications on several real networks from different frameworks and with different order are provided. The performance of our coefficient is also compared with that of existing coefficients.