文章基本信息

标题：Network Features which Affect Information Diffusion
本地全文：下载
作者：SHOHEI USUI ; FUJIO TORIUMI
期刊名称：人工知能学会論文誌
印刷版ISSN：1346-0714
电子版ISSN：1346-8030
出版年度：2015
卷号：30
期号：1
页码：195-203
DOI：10.1527/tjsai.30.195
出版社：The Japanese Society for Artificial Intelligence
摘要：We analyze information diffusion by focusing on network structures. First, we propose a network growth model that produces networks with features required for analysis and perform a validation experiment using Twitter networks. The proposed model produces networks with features calculated from these real networks with high accuracy. Using this proposed model, we produce several networks that exhibit various features. We simulate information diffusion on these networks using an independent cascade (IC) model and calculate the Ability of Information Diffusion ( AID ). Second, we analyze how each feature affects information diffusion using this simulation. We found that the AID score was affected by the average shortest-path length L and variance of closeness centrality σ_ι . We got a high AID score by a network with low L and σ_ι .
关键词：coplex network ; network growth model ; information diffusion ; agent simmulation