An Entropy-Based Self-Adaptive Node Importance Evaluation Method for Complex Networks
Joint Authors
Source
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-13, 13 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-04-25
Country of Publication
Egypt
No. of Pages
13
Main Subjects
Abstract EN
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.
American Psychological Association (APA)
Sun, Qibo& Yang, Guoyu& Zhou, Ao. 2020. An Entropy-Based Self-Adaptive Node Importance Evaluation Method for Complex Networks. Complexity،Vol. 2020, no. 2020, pp.1-13.
https://search.emarefa.net/detail/BIM-1141933
Modern Language Association (MLA)
Sun, Qibo…[et al.]. An Entropy-Based Self-Adaptive Node Importance Evaluation Method for Complex Networks. Complexity No. 2020 (2020), pp.1-13.
https://search.emarefa.net/detail/BIM-1141933
American Medical Association (AMA)
Sun, Qibo& Yang, Guoyu& Zhou, Ao. An Entropy-Based Self-Adaptive Node Importance Evaluation Method for Complex Networks. Complexity. 2020. Vol. 2020, no. 2020, pp.1-13.
https://search.emarefa.net/detail/BIM-1141933
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1141933