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Clustering coefficient

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Clustering coefficient example

Example clustering coefficient on an undirected graph for the shaded node i. Black edges are nodes connecting neighbors of i, and dotted red edges are for unused possible edges.

Duncan J. Watts and Steven Strogatz (1998) introduced the clustering coefficient1 graph measure to determine whether or not a graph is a small-world network.

First, let us define a graph in terms of a set of n vertices V={v_1,v_2,...v_n} and a set of edges E, where e_{ij} denotes an edge between vertices v_i and v_j. Below we assume v_i, v_j and v_k are members of V.

We define the neighbourhood N for a vertex v_i as its immediately connected neighbours as follows:

N_i = \{v_j\} : e_{ij} \in E.

The degree k_i of a vertex is the number of vertices, |N_i|, in its neighbourhood N_i.

The clustering coefficient C_i for a vertex v_i is the proportion of links between the vertices within its neighbourhood divided by the number of links that could possibly exist between them. For a directed graph, e_{ij} is distinct from e_{ji}, and therefore for each neighbourhood N_i there are k_i(k_i-1) links that could exist among the vertices within the neighbourhood. Thus, the clustering coefficient is given as:

C_i = \frac{|\{e_{jk}\}|}{k_i(k_i-1)} : v_j,v_k \in N_i, e_{jk} \in E.

An undirected graph has the property that e_{ij} and e_{ji} are considered identical. Therefore, if a vertex v_i has k_i neighbours, \frac{k_i(k_i-1)}{2} edges could exist among the vertices within the neighbourhood. Thus, the clustering coefficient for undirected graphs can be defined as:

C_i = \frac{2|\{e_{jk}\}|}{k_i(k_i-1)} : v_j,v_k \in N_i, e_{ij} \in E.

Let \lambda_G(v) be the number of triangles on v \in V(G) for undirected graph G. That is, \lambda_G(v) is the number of subgraphs of G with 3 edges and 3 vertices, one of which is v. Let \tau_G(v) be the number of triples on v \in G. That is, \tau_G(v) is the number of subgraphs (not necessarily induced) with 2 edges and 3 vertices, one of which is v and such that v is incident to both edges. Then we can also define the clustering coefficient as:

C_i = \frac{\lambda_G(v)}{\tau_G(v)}

It is simple to show that the two preceding definitions are the same, since \tau_G(v) = C({k_i},2) = \frac{1}{2}k_i(k_i-1).

These measures are 1 if every neighbour connected to v_i is also connected to every other vertex within the neighbourhood, and 0 if no vertex that is connected to v_i connects to any other vertex that is connected to v_i.

The clustering coefficient for the whole system is given by Watts and Strogatz as the average of the clustering coefficient for each vertex:

\overline{C} = \frac{1}{n}\sum_{i=1}^{n} C_i.


1 Watts, D. J. and Strogatz, S. H. "Collective dynamics of 'small-world' networks." [1]

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