A network classification method based on density time evolution patterns extracted from network automata

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MetadadosDescriçãoIdioma
Autor(es): dc.contributorUniversidade de São Paulo (USP)-
Autor(es): dc.contributorUniversidade Estadual Paulista (UNESP)-
Autor(es): dc.creatorZielinski, Kallil M.C.-
Autor(es): dc.creatorRibas, Lucas C.-
Autor(es): dc.creatorMachicao, Jeaneth-
Autor(es): dc.creatorBruno, Odemir M.-
Data de aceite: dc.date.accessioned2025-08-21T18:53:02Z-
Data de disponibilização: dc.date.available2025-08-21T18:53:02Z-
Data de envio: dc.date.issued2025-04-29-
Data de envio: dc.date.issued2024-01-31-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.1016/j.patcog.2023.109946-
Fonte completa do material: dc.identifierhttps://hdl.handle.net/11449/304350-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/304350-
Descrição: dc.descriptionNetwork modeling has proven to be an efficient tool for many interdisciplinary areas, including social, biological, transportation, and various other complex real-world systems. In addition, cellular automata (CA) are a formalism that has received significant attention in recent decades as a model for investigating patterns in the dynamic spatio-temporal behavior of these systems, based on local rules. Some studies investigate the use of cellular automata to analyze the dynamic behavior of networks and refer to them as network automata (NA). Recently, it has been demonstrated that NA is effective for network classification, as it employs a Time-Evolution Pattern (TEP) for feature extraction. However, the TEPs investigated in previous studies consist of binary values (states) that do not capture the intrinsic details of the analyzed network. Therefore, in this work, we propose alternative sources of information that can be used as descriptors for the classification task, which we refer as Density Time-Evolution Pattern (D-TEP) and State Density Time-Evolution Pattern (SD-TEP). We examine the density of alive neighbors of each node, which is a continuous value, and compute feature vectors based on histograms of TEPs. Our results demonstrate significant improvement over previous studies on five synthetic network datasets, as well as seven real datasets. Our proposed method is not only a promising approach for pattern recognition in networks, but also shows considerable potential for other types of data that can be transformed into network.-
Descrição: dc.descriptionConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)-
Descrição: dc.descriptionFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)-
Descrição: dc.descriptionCoordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)-
Descrição: dc.descriptionSão Carlos Institute of Physics University of São Paulo, SP-
Descrição: dc.descriptionInstitute of Biosciences Humanities and Exact Sciences São Paulo State University, SP-
Descrição: dc.descriptionComputer Engineering Department Polytechnic School of the University of São Paulo, SP-
Descrição: dc.descriptionInstitute of Biosciences Humanities and Exact Sciences São Paulo State University, SP-
Descrição: dc.descriptionCNPq: #05610/2022-8-
Descrição: dc.descriptionFAPESP: #2018/22214-6-
Descrição: dc.descriptionFAPESP: #2020/03514-9-
Descrição: dc.descriptionFAPESP: #2021/07289-2-
Descrição: dc.descriptionFAPESP: #2021/08325-2-
Descrição: dc.descriptionFAPESP: #2022/03668-1-
Descrição: dc.descriptionFAPESP: #2023/04583-2-
Descrição: dc.descriptionCAPES: #88887. 631085/2021-00-
Idioma: dc.languageen-
Relação: dc.relationPattern Recognition-
???dc.source???: dc.sourceScopus-
Palavras-chave: dc.subjectCellular automata-
Palavras-chave: dc.subjectComplex networks-
Palavras-chave: dc.subjectNetwork automata-
Palavras-chave: dc.subjectPattern recognition-
Título: dc.titleA network classification method based on density time evolution patterns extracted from network automata-
Tipo de arquivo: dc.typelivro digital-
Aparece nas coleções:Repositório Institucional - Unesp

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