Open AccessHeatmap centrality: A new measure to identify super-spreader nodes in scale-free networks
Author(s) -
Christina Durón
Publication year - 2020
Publication title -
plos one
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.99
H-Index - 332
ISSN - 1932-6203
DOI - 10.1371/journal.pone.0235690
Subject(s) - centrality , shortest path problem , measure (data warehouse) , ranking (information retrieval) , computer science , node (physics) , scale (ratio) , betweenness centrality , scale free network , network analysis , complex network , path (computing) , identification (biology) , data mining , theoretical computer science , artificial intelligence , mathematics , statistics , computer network , graph , geography , biology , physics , botany , cartography , quantum mechanics , world wide web
The identification of potential super-spreader nodes within a network is a critical part of the study and analysis of real-world networks. Motivated by a new interpretation of the “shortest path” between two nodes, this paper explores the properties of the heatmap centrality by comparing the farness of a node with the average sum of farness of its adjacent nodes in order to identify influential nodes within the network. As many real-world networks are often claimed to be scale-free, numerical experiments based upon both simulated and real-world undirected and unweighted scale-free networks are used to illustrate the effectiveness of the proposed “shortest path” based measure with regards to its CPU run time and ranking of influential nodes.