| Full text | |
| Author(s): |
Total Authors: 3
|
| Affiliation: | [1] Univ Sao Paulo, IFSC, Sao Carlos, SP - Brazil
[2] Univ Sao Paulo, FFCLRP, Natl Inst Sci & Technol Complex Syst LNCT SC, BR-14049 Ribeirao Preto, SP - Brazil
Total Affiliations: 2
|
| Document type: | Journal article |
| Source: | Chaos; v. 22, n. 3 SEP 2012. |
| Web of Science Citations: | 7 |
| Abstract | |
Complex networks have attracted increasing interest from various fields of science. It has been demonstrated that each complex network model presents specific topological structures which characterize its connectivity and dynamics. Complex network classification relies on the use of representative measurements that describe topological structures. Although there are a large number of measurements, most of them are correlated. To overcome this limitation, this paper presents a new measurement for complex network classification based on partially self-avoiding walks. We validate the measurement on a data set composed by 40000 complex networks of four well-known models. Our results indicate that the proposed measurement improves correct classification of networks compared to the traditional ones. (C) 2012 American Institute of Physics. {[}http://dx.doi.org/10.1063/1.4737515] (AU) | |
| FAPESP's process: | 11/01523-1 - Computer vision methods applied to the identification and analysis of plants |
| Grantee: | Odemir Martinez Bruno |
| Support Opportunities: | Regular Research Grants |
| FAPESP's process: | 10/08614-0 - Static and Dynamic Texture Analysis and their Applications in Biology and Nanotechnology |
| Grantee: | Wesley Nunes Gonçalves |
| Support Opportunities: | Scholarships in Brazil - Doctorate |