What Is It?
Clustering Coefficient is a network analysis algorithm which describes how closely nodes in a network tend to group or “cluster” together.
Why Is This Important?
Clustering Coefficients provide details relating to the interconnectedness of subcommunities in a network. This metric has proven to be effective for understanding function-structure associations in the brain.
Consider this example involving a Network Graph constructed from the eCommerce sample dataset. Using the Clustering Coefficient, we can see which groups of users have the most unique characteristics in the network. The users in red highlighted here have extremely unique purchasing behaviors that are not found anywhere else in the network.
Steps for running Clustering Coefficient on a Network Graph:
- Right-click anywhere in the plot (not on a node) and hover over Network Analysis Tools, then select Clustering Coefficient.
- The Clustering Coefficient will be calculated and automatically applied to the Color dimension.
In network analysis, it can be useful to know the edge density of portions of the network, especially when a clique is formed (a portion of a network where all the nodes are connected to each other).
Clustering Coefficient is a metric that measures how close a node is to forming a clique with its neighbors. The values for the Clustering Coefficient range from 0 to 1. A node has a Clustering Coefficient of 1 when it forms a clique with its neighbors, while a node has a Clustering Coefficient of 0 when there are no edges among the node’s neighbors.