Immunization strategy for epidemic spreading based on membership (m) over a multilayer network. Issue 2 (23rd August 2019)

• Record Type: Journal Article
• Authors: Singh, Vinay; Verma, Pratima; Muthukumaar, Vignaesh; Kumar, Vimal; Tewari, Manuj; Lai, Kuei‐Kuei; Chang, Yu‐Hsin
• Is Part Of: Business strategy and development. Volume 3:Issue 2(2020)
• Page Start: 185
• Page End: 194
• Publication Date: 2019-08-23
• Subjects: community detection -- complex networks -- epidemic spreading -- immunization strategies -- membership -- rumor spreading -- social networks; Community development -- Periodicals; Sustainable development -- Periodicals; Social responsibility of business -- Periodicals; Economic development -- Periodicals; Social entrepreneurship -- Periodicals; 338.9
• DOI: 10.1002/bsd2.87
• Languages: English
• ISSNs: 2572-3170

Abstract

Abstract: The purpose of this paper is to declare an immunization strategy for epidemic spreading based on membership ( m ) of a node among various communities over a multilayer network. The past few years have seen exponential growth in the understanding and research in modern complex networks field. The mapping of real‐world social interactions to complex networks has opened a new pathway for innovation and research. The real‐world interactions among people take place in various circumstances, and thus, a complex network imitates these interactions using various layers. The events and its information in a real world are incorporated in multiple layers in a network that otherwise are lost due to layer aggregation. The form of interaction captured in a multilayer network can have effects of spreading of a process such as an epidemic. The findings explain the highest membership nodes in one layer, immunizing it and observing the propagation of the epidemic through the layers based on a fraction of overlapped nodes in the network that transmit the disease to various layers. Further, this research paper tried to measure the efficiency of the immunization strategy for epidemic spreading based on membership metric when applied on a multilayer network and henceforth apply them to prevent spreading of the rumor/disease. In this manner, the strategy is more productive to secure the people of the entire system.