2018/05/16 by Chandni Saxena, M. N. Doja, Tanvir Ahmad · 27 citations
Computer Science · Mathematics · Physics and Astronomy · #Artificial intelligence #COVID-19 epidemiological studies #Centrality #Complex Network Analysis Techniques #Computer network #Computer science #Computer security #Game theory #Heuristic #Key (lock) #Mathematical economics #Mathematics #Misinformation #Node (physics) #Opinion Dynamics and Social Influence #Shapley value #cs.SI #physics.soc-ph
paper · pdf · doi:10.1016/j.physa.2018.05.107
published in Physica A Statistical Mechanics and its Applications 508, 35-47 (Elsevier BV)
openalex publication_date 2018/05/16 · arxiv created 2018/12/30 · arxiv updated 2019/01/03 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
Network immunization is an extensively recognized issue in several domains like virtual network security, public health and social media, to deal with the problem of node inoculation so as to minimize the transmission through the links existed in these networks. We aim to identify top ranked nodes to immunize networks, leading to control the outbreak of epidemics or misinformation. We consider group based centrality and define a heuristic objective criteria to establish the target of key nodes finding in network which if immunized result in essential network vulnerability. We propose a group based game theoretic payoff division approach, by employing Shapley value to assign the surplus acquired by participating nodes in different groups through the positional power and functional influence over other nodes. We tag these key nodes as Shapley Value based Information Delimiters (SVID). Experiments on empirical data sets and model networks establish the efficacy of our proposed approach and acknowledge performance of node inoculation to delimit contagion outbreak.