Many empirical networks display an inherent tendency to cluster, i.e., to form circles of connected nodes. This feature is typically measured by the clustering coefficient CC . The CC, originally introduced for binary, undirected graphs, has been recently generalized to weighted, undirected networks. Here we extend the CC to the case of binary and weighted directed networks and we compute its expected value for random graphs. We distinguish between CCs that count all directed triangles in the graph independently of the direction of their edges and CCs that only consider particular types of directed triangles e.g., cycles . The main concepts are illustrated by employing empirical data on world-trade flows.
Clustering in Complex Directed Networks
FAGIOLO, Giorgio
2007-01-01
Abstract
Many empirical networks display an inherent tendency to cluster, i.e., to form circles of connected nodes. This feature is typically measured by the clustering coefficient CC . The CC, originally introduced for binary, undirected graphs, has been recently generalized to weighted, undirected networks. Here we extend the CC to the case of binary and weighted directed networks and we compute its expected value for random graphs. We distinguish between CCs that count all directed triangles in the graph independently of the direction of their edges and CCs that only consider particular types of directed triangles e.g., cycles . The main concepts are illustrated by employing empirical data on world-trade flows.File | Dimensione | Formato | |
---|---|---|---|
PRE_2007.pdf
accesso aperto
Tipologia:
Documento in Post-print/Accepted manuscript
Licenza:
Licenza non conosciuta
Dimensione
656.28 kB
Formato
Adobe PDF
|
656.28 kB | Adobe PDF | Visualizza/Apri |
I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.