Self-similarity in Fractal and Non-fractal Networks

Abstract

We study the origin of scale invariance (SI) of the degree distribution in scale-free (SF) networks with a degree exponent $\gamma$ under coarse graining. A varying number of vertices belonging to a community or a box in a fractal analysis is grouped into a supernode, where the box mass $M$ follows a power-law distribution, $P_m(M)\sim M^{-\eta }$. The renormalized degree $k^{\prime }$ of a supernode scales with its box mass $M$ as $k^{\prime }\sim M^{\theta }$. The two exponents $\eta$ and $\theta$ can be nontrivial as $\eta \ne \gamma$ and $\theta <1$. They act as relevant parameters in determining the self-similarity, i.e., the SI of the degree distribution, as follows: The self-similarity appears either when $\gamma \le \eta$ or under the condition $\theta =(\eta -1)/(\gamma -1)$ when $\gamma >\eta$, irrespective of whether the original SF network is fractal or non-fractal. Thus, fractality and self-similarity are disparate notions in SF networks.

15 pages, 8 figures