Temporal Network Pattern Identification by Community Modelling

Autor(es)

Gao, Xubo ^[1] ; Zheng, Qiusheng ^[1] ; Vega-Oliveros, Didier A. ^{[2, 3]} ; Anghinoni, Leandro ^[4] ; Zhao, Liang ^[3]

Afiliação do(s) autor(es)

^[1] Zhongyuan Univ Technol, Sch Comp Sci, Henan Key Lab Publ Opin Intelligent Anal, Zhengzhou - Peoples R China

^[2] Indiana Univ, Sch Informat Comp & Engn, Bloomington, IN - USA

^[3] Univ Sao Paulo, Fac Philosophy Sci & Letters Ribeirao Preto FFCLR, Ribeirao Preto, SP - Brazil

^[4] Univ Sao Paulo, Inst Math & Comp Sci ICMC USP, Sao Carlos, SP - Brazil

• Tipo de documento: Artigo Científico
• Fonte: SCIENTIFIC REPORTS; v. 10, n. 1 JAN 14 2020.
• Citações Web of Science: 0

Resumo

Temporal network mining tasks are usually hard problems. This is because we need to face not only a large amount of data but also its non-stationary nature. In this paper, we propose a method for temporal network pattern representation and pattern change detection following the reductionist approach. The main idea is to model each stable (durable) state of a given temporal network as a community in a sampled static network and the temporal state change is represented by the transition from one community to another. For this purpose, a reduced static single-layer network, called a target network, is constructed by sampling and rearranging the original temporal network. Our approach provides a general way not only for temporal networks but also for data stream mining in topological space. Simulation results on artificial and real temporal networks show that the proposed method can group different temporal states into different communities with a very reduced amount of sampled nodes. (AU)

• Processo FAPESP: 13/07375-0 - CeMEAI - Centro de Ciências Matemáticas Aplicadas à Indústria
• Beneficiário: Francisco Louzada Neto
• Modalidade de apoio: Auxílio à Pesquisa - Centros de Pesquisa, Inovação e Difusão - CEPIDs
• Processo FAPESP: 18/24260-5 - Análise de dados espaço-temporais baseado em redes complexas
• Beneficiário: Didier Augusto Vega Oliveros
• Modalidade de apoio: Bolsas no Exterior - Estágio de Pesquisa - Pós-Doutorado
• Processo FAPESP: 16/23698-1 - Processos Dinâmicos em Aprendizado de Máquina baseados em Redes Complexas
• Beneficiário: Didier Augusto Vega Oliveros
• Modalidade de apoio: Bolsas no Brasil - Pós-Doutorado
• Processo FAPESP: 15/50122-0 - Fenômenos dinâmicos em redes complexas: fundamentos e aplicações
• Beneficiário: Elbert Einstein Nehrer Macau
• Modalidade de apoio: Auxílio à Pesquisa - Temático