A Model for Generating Random Networks with Clustering Coefficient Corresponding to Real-World Network Graphs

Natarajan Meghanathan
2016 International Journal of Control and Automation  
In this paper, we propose that when a random network graph model that prefers to close two-hop chains and transform them to triangles as part of link formation. We hypothesize that such a model would generate random network graphs with high clustering coefficients and a larger variation in node degree, compared to that of the well-known Erdos-Renyi (ER) model. We refer to the proposed model as two-hop neighbor preference (THNP)-model that prefers to pair a node with any of its two-hop neighbors
more » ... rather than to an arbitrary node. The probability of link formation is still governed by the formulation used to generate links in the ER model. We observe the THNP-model to generate random network graphs wherein the clustering coefficient of a node decreases with increase in node degree (resembling closely to several of the realworld network graphs), and the graphs still exhibit a Poisson-style distribution for the node degree and path length.
doi:10.14257/ijca.2016.9.1.15 fatcat:enlw3dthfrghfoaii7pjicwb7a