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.
Highlights
In recent years, the study of complex network theory has received sustained attention in various academic fields, such as aviation networks [1, 2], power networks [3,4,5], social networks [6,7,8], and biological networks [9,10,11]
The primary contributions of this paper can be summarized as follows: (i) A novel method of node importance ranking in complex networks based on multicriteria decision making is proposed
The entropy weighting (EW)-TOPSIS method is used to identify the top 10 nodes based on the four actual networks, and the three centrality criteria Degree centrality (DC), Closeness centrality (CC), and Betweenness centrality (BC) are used for comparison
Summary
The study of complex network theory has received sustained attention in various academic fields, such as aviation networks [1, 2], power networks [3,4,5], social networks [6,7,8], and biological networks [9,10,11]. Betweenness centrality (BC) [36] measures node importance by means of the ratio of the shortest path over the nodes to the number of all paths It compensates for the limitations of degree centrality and closeness centrality, but problems still exist. The proposed method is based on degree centrality, closeness centrality, and betweenness centrality and computes node importance through integrated computation of these criteria. (i) A novel method of node importance ranking in complex networks based on multicriteria decision making is proposed. It comprehensively combines the advantages of various criteria from different perspectives and makes the measurement more accurate and universal. (iii) Four experiments on four real networks have been conducted, and the experimental results show that the proposed method has superior performance in identifying important nodes in complex networks.
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