ArticlePloS one2012
Recent developments in quantitative graph theory: information inequalities for networks.
Article in PloS one, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
3 citing papers in PubMed.
- Exploring the Entropy Complex Networks with Latent Interaction.Entropy (Basel, Switzerland) · 2023Article
- A Survey of Information Entropy Metrics for Complex Networks.Entropy (Basel, Switzerland) · 2020Review
- Article
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Authors and funding
2 authors.
Funding
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Abstract
In this article, we tackle a challenging problem in quantitative graph theory. We establish relations between graph entropy measures representing the structural information content of networks. In particular, we prove formal relations between quantitative network measures based on Shannon's entropy to study the relatedness of those measures. In order to establish such information inequalities for graphs, we focus on graph entropy measures based on information functionals. To prove such relations, we use known graph classes whose instances have been proven useful in various scientific areas. Our results extend the foregoing work on information inequalities for graphs.
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Registered trials
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