An Entropy-Based Self-Adaptive Node Importance Evaluation Method for Complex Networks

Abstract

Identifying important nodes in complex networks is essential in disease transmission control, network attack protection, and valuable information detection. Many evaluation indicators, such as degree centrality, betweenness centrality, and closeness centrality, have been proposed to identify important nodes. Some researchers assign different weight to different indicator and combine them together to obtain the final evaluation results. However, the weight is usually subjectively assigned based on the researcher’s experience, which may lead to inaccurate results. In this paper, we propose an entropy-based self-adaptive node importance evaluation method to evaluate node importance objectively. Firstly, based on complex network theory, we select four indicators to reflect different characteristics of the network structure. Secondly, we calculate the weights of different indicators based on information entropy theory. Finally, based on aforesaid steps, the node importance is obtained by weighted average method. The experimental results show that our method performs better than the existing methods.

Artificial Intelligence‐Based Real‐Time Signal Sample and Analysis of Multiperson Dragon Boat Race in Complex Networks

“…e complex network theory in network science provides a theoretical basis for the discussion of the above issues [5,6]. e results also provide effective data support for the multilayer perceptron (MLP) model and make the optimistic value obtained more accurate.…”

Evaluation and Analysis Model of Economic Development Level for Latin American Countries

“…To avoid the subjectivity and some objective limitations of artificially determining the weight of indicators and further improve the validity and reliability of this paper, this paper uses the entropy weight method in the objective evaluation method to calculate the weight of each indicator in the evaluation system of high-quality development in the country area. e specific calculation steps [13][14][15][16] are as follows:…”

“…Weights. According to the above calculation steps of the entropy weight method [13][14][15][16], this paper uses the relevant indicator data from 2015 to 2018 of the Latin American countries. e weights for each year are obtained separately, and the average of the four-year weights is used as the comprehensive weight of each indicator.…”

Research on Importance Evaluation of Complex Product Parts Based on Multilayer Complex Network

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