ArticleEntropy (Basel, Switzerland)2019
Key Node Ranking in Complex Networks: A Novel Entropy and Mutual Information-Based Approach.
Article in Entropy (Basel, Switzerland), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Enhanced Key Node Identification in Complex Networks Based on Fractal Dimension and Entropy-Driven Spring Model.Entropy (Basel, Switzerland) · 2025Article
- HA: An Influential Node Identification Algorithm Based on Hub-Triggered Neighborhood Decomposition and Asymmetric Order-by-Order Recurrence Model.Entropy (Basel, Switzerland) · 2025Article
- Identifying Key Nodes for the Influence Spread Using a Machine Learning Approach.Entropy (Basel, Switzerland) · 2024Article
- Exploring the Entropy Complex Networks with Latent Interaction.Entropy (Basel, Switzerland) · 2023Article
- The Structure Entropy-Based Node Importance Ranking Method for Graph Data.Entropy (Basel, Switzerland) · 2023Article
- Identifying Influential Nodes in Complex Networks Based on Multiple Local Attributes and Information Entropy.Entropy (Basel, Switzerland) · 2022Article
- Entropy and Network Centralities as Intelligent Tools for the Investigation of Terrorist Organizations.Entropy (Basel, Switzerland) · 2021Article
- Causality-Network-Based Critical Hazard Identification for Railway Accident Prevention: Complex Network-Based Model Development and Comparison.Entropy (Basel, Switzerland) · 2021Article
- A Survey of Information Entropy Metrics for Complex Networks.Entropy (Basel, Switzerland) · 2020Review
- On the Entropy of Events under Eventually Global Inflated or Deflated Probability Constraints. Application to the Supervision of Epidemic Models under Vaccination Controls.Entropy (Basel, Switzerland) · 2020Article
- Influential Nodes Identification in Complex Networks via Information Entropy.Entropy (Basel, Switzerland) · 2020Article
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Authors and funding
4 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Numerous problems in many fields can be solved effectively through the approach of modeling by complex network analysis. Finding key nodes is one of the most important and challenging problems in network analysis. In previous studies, methods have been proposed to identify key nodes. However, they rely mainly on a limited field of local information, lack large-scale access to global information, and are also usually NP-hard. In this paper, a novel entropy and mutual information-based centrality approach (EMI) is proposed, which attempts to capture a far wider range and a greater abundance of information for assessing how vital a node is. We have developed countermeasures to assess the influence of nodes: EMI is no longer confined to neighbor nodes, and both topological and digital network characteristics are taken into account. We employ mutual information to fix a flaw that exists in many methods. Experiments on real-world connected networks demonstrate the outstanding performance of the proposed approach in both correctness and efficiency as compared with previous approaches.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.