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Offdiagonal complexity: A computationally quick complexity measure for graphs and networks
Jens Christian Claussen
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Corresponding author for this work
College of Engineering and Physical Sciences
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Keyphrases
Random Graphs
100%
Complexity Measures
100%
Link Distribution
100%
Scale-free Network
100%
Complex Structure
50%
Hierarchical Tree
50%
Helicobacter Pylori (H. pylori)
50%
Quantitative Characterization
50%
Graph Structure
50%
Average Path Length
50%
Node-Link
50%
Regular Lattice
50%
Novel Measure
50%
Non-diagonal
50%
Clustering Coefficient
50%
Undirected Graph
50%
Complex Approach
50%
Fully Connected Network
50%
Undirected Network
50%
Power-law Distribution
50%
Protein-protein Interaction Network
50%
Distribution Entropy
50%
Computer Science
Random Graphs
100%
Complexity Measure
100%
Connected Network
50%
Directed Graphs
50%
Average Path Length
50%
Complex Structure
50%
Undirected Network
50%
Power-Law Distribution
50%
Biochemistry, Genetics and Molecular Biology
Quantitative Technique
100%
Cross-Link
100%
Helicobacter Pylori
100%
Protein Interaction
100%
Engineering
Nodes
100%
Complex Structure
50%
Pathlength
50%