Aguarde...
 

LINK PREDICTION IN SOCIAL NETWORKS

ROLE OF POWER LAW DISTRIBUTION


de: R$ 194,65

por: 

R$ 175,19preço +cultura

em até 5x de R$ 35,04 sem juros no cartão, ver mais opções

Produto disponível no mesmo dia no aplicativo Kobo, após a confirmação  do pagamento!

Sinopse

This work presents link prediction similarity measures for social networks that exploit the degree distribution of the networks. In the context of link prediction in dense networks, the text proposes similarity measures based on Markov inequality degree thresholding (MIDTs), which only consider nodes whose degree is above a threshold for a possible link. Also presented are similarity measures based on cliques (CNC, AAC, RAC), which assign extra weight between nodes sharing a greater number of cliques. Additionally, a locally adaptive (LA) similarity measure is proposed that assigns different weights to common nodes based on the degree distribution of the local neighborhood and the degree distribution of the network. In the context of link prediction in dense networks, the text introduces a novel two-phase framework that adds edges to the sparse graph to forma boost graph.

Detalhes do Produto

    • Ano de Edição: 2016
    • Ano:  2016
    • País de Produção: Canada
    • Código de Barras:  2000994436401
    • ISBN:  9783319289229

Avaliação dos Consumidores

ROLAR PARA O TOPO