Advanced search
Start date
Betweenand
Related content
(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

Life-Like Network Automata descriptor based on binary patterns for network classification

Full text
Author(s):
Ribas, Lucas C. [1] ; Machicao, Jeaneth [2] ; Bruno, Odemir M. [1, 2]
Total Authors: 3
Affiliation:
[1] Univ Sao Paulo, Inst Math & Comp Sci ICMC, POB 668, BR-14560970 Sao Carlos, SP - Brazil
[2] Univ Sao Paulo, Sao Carlos Inst Phys, Sci Comp Grp, POB 369, BR-14560970 Sao Carlos, SP - Brazil
Total Affiliations: 2
Document type: Journal article
Source: INFORMATION SCIENCES; v. 515, p. 156-168, APR 2020.
Web of Science Citations: 0
Abstract

We propose a descriptor based on binary patterns extracted from network-automata time-evolution patterns (TEP) aiming to characterize networks. More, in particular, we explore TEPs descriptors from the Life-Like Network Automata (LLNA), a cellular automaton inspired by the rules of the ``Life-Like{''} family that uses a network as tessellation, and based on its dynamics to extract features for network characterization. In recent work, the LLNA has been introduced as a pattern recognition tool that uses a descriptor based on the histograms of complexity measures such as the entropy, word length, and Lempel-Ziv complexity. However, these descriptors correspond to continuous values, and consequently, their histograms lack of an optimal number of bins, which therefore turns out to be a parametric issue. To overcome this disadvantage, we propose a new descriptor that computes feature vectors formed by discrete binary patterns histograms with different lengths D. Furthermore, we show a statistical improvement of the proposed method compared to earlier approaches such as the original LLNA and classical network structural measurements. Our experimental results show the performance improvement of the proposed method in six synthetic network databases and eight real network databases. (C) 2019 Elsevier Inc. All rights reserved. (AU)

FAPESP's process: 16/23763-8 - Modeling and analysis of complex networks for computer vision
Grantee:Lucas Correia Ribas
Support Opportunities: Scholarships in Brazil - Doctorate
FAPESP's process: 16/18809-9 - Deep learning and complex networks applied to computer vision
Grantee:Odemir Martinez Bruno
Support Opportunities: Research Grants - Research Partnership for Technological Innovation - PITE
FAPESP's process: 14/08026-1 - Artificial vision and pattern recognition applied to vegetal plasticity
Grantee:Odemir Martinez Bruno
Support Opportunities: Regular Research Grants