Structure of cycles and local ordering in complex networks
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Summary
The analysis of data from real networks reveals the ubiquitous presence of a statistically high level of grid-like ordering that is non-trivially correlated with the local degree properties, providing new insights on the hierarchical structure of complex networks.
- Type
- article
- Published
- 2004-03-01
- Cited by
- 80
- References
- 31
- Access
- Open access
- OpenAlex
- https://openalex.org/W2108003960
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:6206858
Keywords
Complex network, Local structure, Grid, Computer science, Cluster analysis
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- The yeast protein interaction network evolves rapidly and contains few redundant duplicate genes.
Cited by
- Decoding the urban grid: or why cities are neither trees nor perfect grids
- Bayesian mixed-membership models of complex and evolving networks
- Structural and Dynamical Properties of Complex Networks.
- Characteristics of Small Social Networks
- The p-index: theory and applications
- RDBMS as an Efficient Tool to Mine Cliques on Complex Networks
- Lamellar pattern formation in small-world media
- Complete trails of coauthorship network evolution.
- Cycles and clustering in bipartite networks.
- Self-similarity, small-world, scale-free scaling, disassortativity, and robustness in hierarchical lattices
- The properties of high-dimensional data spaces: implications for exploring gene and protein expression data
- Loops of any size and Hamilton cycles in random scale-free networks
- Prisoner's Dilemma Game on Clustered Scale-Free Networks under Different Initial Distributions
- Characterization of complex networks: A survey of measurements
- Coevolution with weights of names in structured language games
- Towards a Precise and Complete Internet Topology Generator
- Characterization of complex networks by higher order neighborhood properties
- Random graph model with power-law distributed triangle subgraphs.
- Network community structure and loop coefficient method.
- SNAVI: Desktop application for analysis and visualization of large-scale signaling networks
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