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Evolving networks by merging cliques
Kazuhiro Takemoto1, Chikoo Oosawa
1Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, Iizuka Fukuoka 820-8502, Japan. d673050k@bio.kyutech.ac.jp
Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics
|December 31, 2005
Summary
We present a new network evolution model using merging graph blocks. This tunable model generates networks with power-law properties and matches the Barabási-Albert model in specific cases.
Area of Science:
- Network Science
- Graph Theory
- Complex Systems
Background:
- Understanding evolving networks is crucial in various fields like biology and sociology.
- Existing models often focus on specific growth mechanisms.
Purpose of the Study:
- To introduce a novel model for network evolution based on merging complete graph modules.
- To analyze the emergent properties and tunability of this model.
Main Methods:
- Developing a model where networks grow by merging complete graph building blocks.
- Deriving analytical solutions for network properties like degree distribution and clustering.
- Comparing model outcomes with classical network models such as Erdös-Rényi and Barabási-Albert.
Main Results:
- The model exhibits power-law degree distributions and clustering spectra.
- High average clustering coefficients are observed, independent of network size.
- The degree exponent is shown to be tunable based on the ratio of merging nodes.
Conclusions:
- The proposed model offers a flexible framework for generating complex evolving networks.
- It successfully replicates key properties of real-world networks and known models.
- The tunability of network exponents is a significant feature for diverse applications.