Avg Clustering Coefficient

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Avg Clustering Coefficient

Galternatives.png This metric is implemented in 0.7 and 0.8.


Linuxdcpp.png Node metric



The clustering coefficient (Watts-Strogatz), when applied to a single node, is a measure of how complete the neighborhood of a node is. When applied to an entire network, it is the average clustering coefficient over all of the nodes in the network.

Objective

The clustering coefficient, along with the mean shortest path, can indicate a "small-world" effect.

For the clustering coefficient to be meaningful it should be significantly higher than in version of the network where all of the edges have been shuffled.

Description

The neighborhood of a node, u, is the set of nodes that are connected to u. If every node in the neighborhood of u is connected to every other node in the neighborhood of u, then the neighborhood of u is complete and will have a clustering coefficient of 1. If no nodes in the neighborhood of u are connected, then the clustering coefficient will be 0.

Source code

See org.gephi.statistics.plugin.ClusteringCoefficient.java.

Acknowledgements

This code was implemented by Patrick McSweeney.

Implemented Algorithm

Matthieu Latapy, Main-memory Triangle Computations for Very Large (Sparse (Power-Law)) Graphs, in Theoretical Computer Science (TCS) 407 (1-3), pages 458-473, 2008. PDF

Other References

  • Watts, D.J., Strogatz, S.H.(1998) Collective dynamics of 'small-world' networks. Nature 393:440-442. PDF

Additional Reading