Percolation and Loop Statistics in Complex Networks
| dc.creator | Noh, Jae Dong | |
| dc.date | 2007-07-04 | |
| dc.date | 2007-08-14 | |
| dc.date.accessioned | 2026-07-07T12:04:39Z | |
| dc.date.available | 2026-07-07T12:04:39Z | |
| dc.description | Complex networks display various types of percolation transitions. We show that the degree distribution and the degree-degree correlation alone are not sufficient to describe diverse percolation critical phenomena. This suggests that a genuine structural correlation is an essential ingredient in characterizing networks. As a signature of the correlation we investigate a scaling behavior in $M_N(h)$, the number of finite loops of size $h$, with respect to a network size $N$. We find that networks, whose degree distributions are not too broad, fall into two classes exhibiting $M_N(h)\sim ({constant})$ and $M_N(h) \sim (\ln N)^ψ$, respectively. This classification coincides with the one according to the percolation critical phenomena. | |
| dc.description | 4 pages and 2 figures; A major revision has been made | |
| dc.identifier | https://arxiv.org/abs/0707.0560 | |
| dc.identifier | http://arxiv.org/abs/0707.0560 | |
| dc.identifier | Eur. Phys. J. B 66, 251 (2008) | |
| dc.identifier | doi:10.1140/epjb/e2008-00401-9 | |
| dc.identifier.uri | http://salesiana.dossiersoluciones.com/handle/123456789/208162 | |
| dc.subject | Statistical Mechanics | |
| dc.title | Percolation and Loop Statistics in Complex Networks | |
| dc.type | text |