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(Referência obtida automaticamente do Web of Science, por meio da informação sobre o financiamento pela FAPESP e o número do processo correspondente, incluída na publicação pelos autores.)

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

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Autor(es):
Ribas, Lucas C. [1] ; Machicao, Jeaneth [2] ; Bruno, Odemir M. [1, 2]
Número total de Autores: 3
Afiliação do(s) autor(es):
[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
Número total de Afiliações: 2
Tipo de documento: Artigo Científico
Fonte: INFORMATION SCIENCES; v. 515, p. 156-168, APR 2020.
Citações Web of Science: 0
Resumo

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)

Processo FAPESP: 16/23763-8 - Modelagem e análise de redes complexas para visão computacional
Beneficiário:Lucas Correia Ribas
Linha de fomento: Bolsas no Brasil - Doutorado
Processo FAPESP: 16/18809-9 - Deep learning e redes complexas aplicados em visão computacional
Beneficiário:Odemir Martinez Bruno
Linha de fomento: Auxílio à Pesquisa - Parceria para Inovação Tecnológica - PITE
Processo FAPESP: 14/08026-1 - Visão artificial e reconhecimento de padrões aplicados em plasticidade vegetal
Beneficiário:Odemir Martinez Bruno
Linha de fomento: Auxílio à Pesquisa - Regular