Scale-free Networks without Growth or Preferential Attachment: Good get Richer
| dc.creator | Caldarelli, G. | |
| dc.creator | Capocci, A. | |
| dc.creator | Rios, P. De Los | |
| dc.creator | Munoz, M. A. | |
| dc.date | 2002-07-15 | |
| dc.date | 2002-10-28 | |
| dc.date.accessioned | 2026-07-07T02:46:16Z | |
| dc.date.available | 2026-07-07T02:46:16Z | |
| dc.description | A new mechanism leading to scale-free networks is proposed in this letter. It is shown that in many cases of interest, the connectivity power-law behavior is neither related to dynamical properties nor to preferential attachment. Instead, we show that without increasing the number of vertices in time and without applying the so called {\it ``rich-get-richer''} condition we obtain networks whose statistical properties are scale-free. Assigning a quenched fitness value $x_i$ to every vertex, and drawing links among vertices with a probability depending on the fitnesses of the two involved sites, gives rise to what we call a {\it ``good-get-richer''} mechanism, in which sites with larger fitness are more likely to become hubs (i.e., to be highly connected). This procedure generates power-law behaviors for various fitness distributions and attaching rules. | |
| dc.description | 4 pages, 4 figures, revtex. Accepted for publication. Minor corrections added | |
| dc.identifier | https://arxiv.org/abs/cond-mat/0207366 | |
| dc.identifier | http://arxiv.org/abs/cond-mat/0207366 | |
| dc.identifier.uri | http://salesiana.dossiersoluciones.com/handle/123456789/19607 | |
| dc.subject | Disordered Systems and Neural Networks | |
| dc.title | Scale-free Networks without Growth or Preferential Attachment: Good get Richer | |
| dc.type | text |