dorsal/arxiv
View SchemaClustering in Complex Directed Networks
| Authors | Giorgio Fagiolo |
|---|---|
| Categories | |
| ArXiv ID | physics/0612169 |
| URL | https://arxiv.org/abs/physics/0612169 |
| DOI | 10.1103/PhysRevE.76.026107 |
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.
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"abstract": "Many empirical networks display an inherent tendency to cluster, i.e. to form\ncircles of connected nodes. This feature is typically measured by the\nclustering coefficient (CC). The CC, originally introduced for binary,\nundirected graphs, has been recently generalized to weighted, undirected\nnetworks. Here we extend the CC to the case of (binary and weighted) directed\nnetworks and we compute its expected value for random graphs. We distinguish\nbetween CCs that count all directed triangles in the graph (independently of\nthe direction of their edges) and CCs that only consider particular types of\ndirected triangles (e.g., cycles). The main concepts are illustrated by\nemploying empirical data on world-trade flows.",
"arxiv_id": "physics/0612169",
"authors": [
"Giorgio Fagiolo"
],
"categories": [
"physics.soc-ph"
],
"doi": "10.1103/PhysRevE.76.026107",
"title": "Clustering in Complex Directed Networks",
"url": "https://arxiv.org/abs/physics/0612169"
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