Epidemic Spreading and Localization in Multilayer Scale-free Networks

• Item type: Journal(1)
• 公開日: 2023-02-15
• タイトル: Epidemic Spreading and Localization in Multilayer Scale-free Networks
• タイトル / 言語: en
• タイトル: Epidemic Spreading and Localization in Multilayer Scale-free Networks
• 言語: eng
• キーワード / 主題Scheme: Other
• キーワード / 主題: [一般論文] epidemic spreading, SIS, privacy, complex networks, scale-free networks, multilayer networks, BA networks, mixing, localization
• 資源タイプ / 資源タイプ識別子: http://purl.org/coar/resource_type/c_6501
• 資源タイプ: journal article
• 著者所属: Department of Business Administration, Faculty of Economics, the International University of Kagoshima
• 著者所属(英): en
• 著者所属(英): Department of Business Administration, Faculty of Economics, the International University of Kagoshima
• 著者名: Atuya, Okudaira
• 著者名(英): Atuya, Okudaira
• 論文抄録 / 内容記述タイプ: Other
• 論文抄録 / 内容記述: The creation and spreading of new pathogens by pathogen-pathogen interaction were modeled and computer-simulated using the Susceptible Infected Susceptible (SIS) model on three-layer scale-free networks: the Barabási-Albert (BA) networks, the Dorogovtsev-Mendes-Samukhin (DMS) networks and the shuffled BA networks. The new pathogen is a model of a new virus, critical privacy information (old pathogens are uncritical data) etc. When the emergence of a new pathogen was low, bimodal metastable states were observed. They had high states and low states of the prevalence. The high states were metastable states with positive prevalence. The low states have the prevalence fluctuating near 0. On both shuffled and non-shuffled networks, the epidemic prevalence in the metastable state on the BA networks are well explained by the the heterogeneous mean-field (HMF) formulation of the SIS+g model. The model does not match well on the DMS networks; this may be because their connectivity distributions are convex or concave. By the three-layer simulation, the infected nodes in layer-0 and layer-1 were found to be not always well-mixed i.e. they are localized. The localization occurred when the epidemic prevalence was low. The nodes with medium connectivity contributed to it. The meaning of results for privacy is discussed.; ------------------------------; This is a preprint of an article intended for publication Journal of; Information Processing(JIP). This preprint should not be cited. This; article should be cited as: Journal of Information Processing Vol.31(2023) (online); DOI http://dx.doi.org/10.2197/ipsjjip.31.97; ------------------------------
• 論文抄録(英) / 内容記述タイプ: Other
• 論文抄録(英) / 内容記述: The creation and spreading of new pathogens by pathogen-pathogen interaction were modeled and computer-simulated using the Susceptible Infected Susceptible (SIS) model on three-layer scale-free networks: the Barabási-Albert (BA) networks, the Dorogovtsev-Mendes-Samukhin (DMS) networks and the shuffled BA networks. The new pathogen is a model of a new virus, critical privacy information (old pathogens are uncritical data) etc. When the emergence of a new pathogen was low, bimodal metastable states were observed. They had high states and low states of the prevalence. The high states were metastable states with positive prevalence. The low states have the prevalence fluctuating near 0. On both shuffled and non-shuffled networks, the epidemic prevalence in the metastable state on the BA networks are well explained by the the heterogeneous mean-field (HMF) formulation of the SIS+g model. The model does not match well on the DMS networks; this may be because their connectivity distributions are convex or concave. By the three-layer simulation, the infected nodes in layer-0 and layer-1 were found to be not always well-mixed i.e. they are localized. The localization occurred when the epidemic prevalence was low. The nodes with medium connectivity contributed to it. The meaning of results for privacy is discussed.; ------------------------------; This is a preprint of an article intended for publication Journal of; Information Processing(JIP). This preprint should not be cited. This; article should be cited as: Journal of Information Processing Vol.31(2023) (online); DOI http://dx.doi.org/10.2197/ipsjjip.31.97; ------------------------------
• 書誌レコードID / 収録物識別子タイプ: NCID
• 書誌レコードID / 収録物識別子: AN00116647
• 書誌情報: 情報処理学会論文誌; 巻 64, 号 2, 発行日 2023-02-15
• ISSN / 収録物識別子タイプ: ISSN
• ISSN / 収録物識別子: 1882-7764
• 公開者 / 言語: ja
• 公開者 / 出版者: 情報処理学会