Publication
Title
Cluster diversity and entropy on the percolation model : the lattice animal identification algorithm
Author
Abstract
We present an algorithm to identify and count different lattice animals (LA's) in the site-percolation model. This algorithm allows a definition of clusters based on the distinction of cluster shapes, in contrast with the well-known Hoshen-Kopelman algorithm, in which the clusters are differentiated by their sizes. It consists in coding each unit. cell of a cluster according to the nearest neighbors (NN) and ordering the codes in a proper sequence. In this manner, a LA is represented by a specific code sequence. In addition, with some modification the algorithm is capable of differentiating between fixed and free LA's. The enhanced Hoshen-Kopelman algorithm [J. Hoshen, M. W. Berry, and K. S. Minser, Phys. Rev. E 56, 1455 (1997)] is used to compose the set of NN code sequences of each cluster. Using Monte Carlo simulations on planar square lattices up to 2000x2000, we apply this algorithm to the percolation model. We calculate the cluster diversity and cluster entropy of the system, which leads to the determination of probabilities associated with the maximum of these functions. We show that these critical probabilities are associated with the percolation transition and with the complexity of the system.
Language
English
Source (journal)
Physical review : E : statistical physics, plasmas, fluids, and related interdisciplinary topics. - Lancaster, Pa, 1993 - 2000
Publication
Lancaster, Pa : 2000
ISSN
1063-651X [print]
1095-3787 [online]
Volume/pages
62:5 Part a(2000), p. 6004-6014
ISI
000165341700025
Full text (Publisher's DOI)
Full text (open access)
UAntwerpen
Faculty/Department
Research group
Publication type
Subject
Affiliation
Publications with a UAntwerp address
External links
Web of Science
Record
Identification
Creation 03.01.2013
Last edited 25.07.2017
To cite this reference