Evaluating link prediction by diffusion processes in dynamic networks

Autor(es)

Vega-Oliveros, Didier A. ^{[1, 2]} ; Zhao, Liang ^[1] ; Berton, Lilian ^[3]

Afiliação do(s) autor(es)

^[1] Univ Sao Paulo, Dept Comp & Math, Ribeirao Preto, SP - Brazil

^[2] Indiana Univ, Sch Informat Comp & Engn, Bloomington, IN 47405 - USA

^[3] Univ Fed Sao Paulo, Inst Sci & Technol, Sao Jose Dos Campos, SP - Brazil

• Tipo de documento: Artigo Científico
• Fonte: SCIENTIFIC REPORTS; v. 9, JUL 25 2019.
• Citações Web of Science: 0

Resumo

Link prediction (LP) permits to infer missing or future connections in a network. The network organization defines how information spreads through the nodes. In turn, the spreading may induce changes in the connections and speed up the network evolution. Although many LP methods have been reported in the literature, as well some methodologies to evaluate them as a classification task or ranking problem, none have systematically investigated the effects on spreading and the structural network evolution. Here, we systematic analyze LP algorithms in a framework concerning: (1) different diffusion process - Epidemics, Information, and Rumor models; (2) which LP method most improve the spreading on the network by the addition of new links; (3) the structural properties of the LP-evolved networks. From extensive numerical simulations with representative existing LP methods on different datasets, we show that spreading improve in evolved scale-free networks with lower shortest-path and structural holes. We also find that properties like triangles, modularity, assortativity, or coreness may not increase the propagation. This work contributes as an overview of LP methods and network evolution and can be used as a practical guide of LP methods selection and evaluation in terms of computational cost, spreading capacity and network structure. (AU)

• 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: 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: 18/01722-3 - Aprendizado semissupervisionado via redes complexas: construção de redes, seleção e propagação de rótulos e aplicações
• Beneficiário: Lilian Berton
• Modalidade de apoio: Auxílio à Pesquisa - Regular
• 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