Results 1 to 10 of about 45,003 (116)
Sub-graph entropy based network approaches for classifying adolescent obsessive-compulsive disorder from resting-state functional MRI [PDF]
NeuroImage: Clinical, 2020 This paper presents a novel approach for classifying obsessive-compulsive disorder (OCD) in adolescents from resting-state fMRI data. Currently, the state-of-the-art for diagnosing OCD in youth involves interviews with adolescent patients and their ...
Bhaskar Sen +4 more
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Relating Vertex and Global Graph Entropy in Randomly Generated Graphs [PDF]
Entropy, 2018 Combinatoric measures of entropy capture the complexity of a graph but rely upon the calculation of its independent sets, or collections of non-adjacent vertices. This decomposition of the vertex set is a known NP-Complete problem and for most real world
Philip Tee +3 more
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Some New Properties for Degree-Based Graph Entropies
Entropy, 2015 The graph entropies inspired by Shannon’s entropy concept become the information-theoretic quantities for measuring the structural information of graphs and complex networks.
Guoxiang Lu, Bingqing Li, Lijia Wang
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Degree-Based Graph Entropy in Structure–Property Modeling [PDF]
Entropy, 2023 Sourav Mondal, Kinkar Chandra Das
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Entropy, 2021 Functional modules can be predicted using genome-wide protein–protein interactions (PPIs) from a systematic perspective. Various graph clustering algorithms have been applied to PPI networks for this task.
Hoyeon Jeong +4 more
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Graph Entropy Based on Strong Coloring of Uniform Hypergraphs
Axioms, 2021 The classical graph entropy based on the vertex coloring proposed by Mowshowitz depends on a graph. In fact, a hypergraph, as a generalization of a graph, can express complex and high-order relations such that it is often used to model complex systems ...
Lusheng Fang +3 more
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On Entropy of Some Fractal Structures
Fractal and Fractional, 2023 Shannon entropy, also known as information entropy or entropy, measures the uncertainty or randomness of probability distribution. Entropy is measured in bits, quantifying the average amount of information required to identify an event from the ...
Haleemah Ghazwani +3 more
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Recent developments in quantitative graph theory: information inequalities for networks. [PDF]
PLoS ONE, 2012 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.
Matthias Dehmer, Lavanya Sivakumar
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Domain Concept Clustering Method Based on Graph Entropy Extreme Value Theory [PDF]
Jisuanji gongcheng, 2020 In domain ontology learning,in order to implement optimal clustering of concepts of the same domain without concept overlapping,this paper introduces the graph entropy extreme value theory and proposes a domain concept clustering method.According to the ...
AN Jingmin, LI Guanyu
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Entropy bounds for hierarchical molecular networks. [PDF]
PLoS ONE, 2008 In this paper we derive entropy bounds for hierarchical networks. More precisely, starting from a recently introduced measure to determine the topological entropy of non-hierarchical networks, we provide bounds for estimating the entropy of hierarchical ...
Matthias Dehmer +2 more
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