6th European Conference - Academic Conferences

2.2 Outdegree
Manoj Cherukuri and Srinivas Mukkamala
Outdegree of a node is defined as the number of edges pointing out from a node. For example, the
outdegree of node A in Figure 3 is 3 since there are three edges emerging from A pointing towards
nodes B, C, D.
Figure 3: Graph demonstrating node A with outdegree 3
2.3 Clustering coefficient
Clustering coefficient is the measure of degree of closeness among the nodes of a graph (Clustering
Coefficient, 2010). Chakrabarti and Faloutsos (2006) stated that the clustering coefficient represents
the clumpiness of the graph. Clustering coefficient of a node is computed as the ratio of number of
links among the linked nodes of a node to the number of possible links among the linked nodes of a
node. The clustering coefficient of the nodes with 0 or 1 neighbors is 0.
Clustering coefficient of all the nodes are computed and averaged to get the clustering coefficient of
the network. For example, consider the graph shown in the Figure 4.
Node A has three neighbors namely, B, C and D. BC is the only link among the neighbors of A.
Number of possible links among the neighbors of A are 3 (i.e. 3 C2). Therefore, the clustering
coefficient of A is 0.33.
Node B has two neighbors and there is one link among the neighbors of B. Therefore, the
clustering coefficient of B is 1.
Node C has two neighbors and there is one link among the neighbors of C. Therefore, the
clustering coefficient of C is 1.
Node D has two neighbors and there is no link among the neighbors of D. Therefore, the
clustering coefficient of D is 0.
Node E has one neighbor and there is one link among the neighbors of E. Therefore, the
clustering coefficient of E is 0.
Figure 4: Graph used for explaining clustering coefficient
The clustering coefficient of a graph is computed using the following formula,
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