# An analytic derivation of clustering coefficients for weighted networks

@article{Zhang2010AnAD, title={An analytic derivation of clustering coefficients for weighted networks}, author={Yichao Zhang and Zhongzhi Zhang and Jihong Guan and Shuigeng Zhou}, journal={Journal of Statistical Mechanics: Theory and Experiment}, year={2010}, volume={2010}, pages={P03013} }

Clustering coefficients are among the most important parameters characterizing the topology of complex networks and have a significant influence on various dynamical processes occurring on networks. On the other hand, a plethora of real-life networks with diverse links can be described better in terms of weighted networks than in terms of binary networks, where all links are homogeneous. However, analytical research on clustering coefficients in weighted networks is still lacking. In this paper…

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## References

SHOWING 1-10 OF 62 REFERENCES

Mutual selection model for weighted networks.

- Computer SciencePhysical review. E, Statistical, nonlinear, and soft matter physics
- 2005

A mutual selection model to characterize the weighted networks is proposed and can produce power-law distributions of degree, weight, and strength, as confirmed in many real networks.

Evolution of networks

- Computer Science
- 2002

The recent rapid progress in the statistical physics of evolving networks is reviewed, and how growing networks self-organize into scale-free structures is discussed, and the role of the mechanism of preferential linking is investigated.

Modeling the evolution of weighted networks.

- Computer SciencePhysical review. E, Statistical, nonlinear, and soft matter physics
- 2004

A general model for the growth of weighted networks in which the structural growth is coupled with the edges' weight dynamical evolution, which yields a nontrivial time evolution of vertices' properties and scale-free behavior with exponents depending on the microscopic parameters characterizing the coupling rules.

The architecture of complex weighted networks.

- Computer ScienceProceedings of the National Academy of Sciences of the United States of America
- 2004

This work studies the scientific collaboration network and the world-wide air-transportation network, which are representative examples of social and large infrastructure systems, respectively, and defines appropriate metrics combining weighted and topological observables that enable it to characterize the complex statistical properties and heterogeneity of the actual strength of edges and vertices.

Mean-field theory for clustering coefficients in Barabási-Albert networks.

- Computer SciencePhysical review. E, Statistical, nonlinear, and soft matter physics
- 2003

It is found that the local clustering in BA networks depends on the node degree, and clustering coefficient of a whole network calculated from the mean-field approach perfectly fits numerical data.

Characterization of complex networks: A survey of measurements

- Computer Science
- 2005

This article presents a survey of measurements capable of expressing the most relevant topological features of complex networks and includes general considerations about complex network characterization, a brief review of the principal models, and the presentation of the main existing measurements.

Emergence of weight-topology correlations in complex scale-free networks

- Computer Science
- 2005

It is shown that networks with and without weight-topology correlations can emerge from the same simple growth dynamics of the nodes connectivities and of the links weights.

The effects of spatial constraints on the evolution of weighted complex networks

- Computer Science
- 2005

The presented results suggest that the interplay between weight dynamics and spatial constraints is a key ingredient in order to understand the formation of real-world weighted networks.

Statistical mechanics of complex networks

- Computer ScienceArXiv
- 2001

A simple model based on these two principles was able to reproduce the power-law degree distribution of real networks, indicating a heterogeneous topology in which the majority of the nodes have a small degree, but there is a significant fraction of highly connected nodes that play an important role in the connectivity of the network.