Growing Scale-Free Networks with Tunable Clustering

Details Growing Scale-Free Networks with Tunable Clustering Growing Scale-Free Networks with Tunable Clustering

2001-10-22

arxiv.org/abs/cond-mat/0110452v1

Phys. Rev. E 65, 026107 (2002).

Physics

Condensed Matter

Disordered Systems and Neural Networks

Accepted for publication in Phys. Rev. E

Scientific paper

10.1103/PhysRevE.65.026107

We extend the standard scale-free network model to include a ``triad formation step''. We analyze the geometric properties of networks generated by this algorithm both analytically and by numerical calculations, and find that our model possesses the same characteristics as the standard scale-free networks like the power-law degree distribution and the small average geodesic length, but with the high-clustering at the same time. In our model, the clustering coefficient is also shown to be tunable simply by changing a control parameter - the average number of triad formation trials per time step.

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