Node Importance Ranking in Complex Networks Based on Multicriteria Decision Making

Abstract

Measuring node importance in complex networks has great theoretical and practical significance for network stability and robustness. A variety of network centrality criteria have been presented to address this problem, but each of them focuses only on certain aspects and results in loss of information. Therefore, this paper proposes a relatively comprehensive and effective method to evaluate node importance in complex networks using a multicriteria decision-making method. This method not only takes into account degree centrality, closeness centrality, and betweenness centrality, but also uses an entropy weighting method to calculate the weight of each criterion, which can overcome the influence of the subjective factor. To illustrate the effectiveness and feasibility of the proposed method, four experiments were conducted to rank node importance on four real networks. The experimental results showed that the proposed method can rank node importance more comprehensively and accurately than a single centrality criterion.

Study on the Localization of Fangcang Shelter Hospitals During Pandemic Outbreaks

“…It sorts the evaluation objects according to their consistency to the ideal solution and evaluates the relative merits among the existing evaluation objects ( 44 ). TOPSIS accredits equal weights to each criterion, whereby various criteria would play the different roles during the procedure are ignored ( 45 ). In this study, EWM is adopted to calculate the weight of each criterion and reduce the disadvantages of TOPSIS, which adopts equal weights.…”

Multi-Criteria Seed Selection for Targeting Multi-Attribute Nodes in Complex Networks

“…Zareie et al [19] used the TOPSIS method (Technique for Order Preference by Similarity to Ideal Solution) to reduce overlap and maximize coverage while influencing social networks. Yang et al [20] used TOPSIS in the Susceptible-Infected-Recovered (SIR) model to dynamically identify influential nodes in complex networks, and in [21] used entropy weighting for setting the weights values. Liu et al [22] used TOPSIS to evaluate the importance of nodes in Shanxi water network and Beijing subway networks by comparing each node's close degree to an ideal object.…”

Heatmap centrality: A new measure to identify super-spreader nodes in scale-free networks

“…In theory, the SI model identifies influential nodes based upon the idea that an influential node is more likely to have a role in passing along a disease (or analogously, information), and thus, have a stronger spreading capability [48]. The SI model has been used as a baseline model to compare the rankings of centrality measures [35][36][37][38], where the average infection efficiency of nodes is used as a measure to evaluate the effectiveness of a centrality measure [49].…”

“…To evaluate the algorithmic performance and efficiency on simulated scale-free networks, the CPU (central processing unit) times and the Spearman-rank and Kendall-rank correlation coefficients of the proposed heatmap measure are calculated on networks of various size N and density d. To evaluate the algorithmic efficiency on real-world scale-free networks, three experiments are performed to compare the nodal ranking of the proposed measure with the rankings with respect to the degree, eigenvector, closeness, and betweenness centralities: A comparison of the top-10 ranked nodes, a comparison of both the Spearman-rank and Kendall-rank correlation coefficients between each pair of measures, and a comparison of the spreading capability of the top-10 nodes using a modification of the standard Susceptible-Infected (SI) model [35][36][37][38]. Based upon the results of the experiments performed on both the simulated and real-world scale-free networks, the heatmap centrality may be considered as a potentially viable measure in the identification of super-spreader nodes in scale-free networks.…”