| Autor: |
Natarajan Meghanathan, Aniekan Essien, Raven Lawrence |
| Jazyk: |
angličtina |
| Rok vydání: |
2021 |
| Předmět: |
|
| Zdroj: |
Egyptian Informatics Journal, Vol 22, Iss 3, Pp 389-400 (2021) |
| Druh dokumentu: |
article |
| ISSN: |
1110-8665 |
| DOI: |
10.1016/j.eij.2016.06.008 |
| Popis: |
The Erdos-Renyi (ER) random network model generates graphs under the assumption that there could exist a link u-v between two nodes u and v irrespective of whether or not the two nodes had a common neighbor before the establishment of the link. As a result, random network graphs generated under the ER model are characteristic of having a low clustering coefficient (a measure of the probability for a link to exist between any two neighbors of a node) and low variation in the node degrees, and hence could not match closely to graphs abstracting real-world networks. In this paper, we propose a random network graph model that gives preference to closing the triangle involving three nodes u, w and v with existing links u-w and w-v (i.e., node v is strictly a two-hop neighbor of node u). Accordingly, when node u is looking for a new link to be setup with some other node x, we consider x along with the two-hop neighbors of u and choose one among these nodes with a probability plink as the new neighbor of node u. The proposed Two-Hop Neighbor Preference (THNP)-based model generates random graphs whose clustering coefficient decreases with increase in node degree: matching closely to several real-world network graphs that are commonly studied for complex network analysis. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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