Real-world networks are complex and rich in variety, as explained above; a measure of the structural complexity of networks (network complexity) is therefore necessary.
The “graph entropy” is often used to evaluate network complexity (reviewed in Refs. [45,46]). The graph entropy of network G is based on Shannon's entropy, and it is conceptually defined as
(2.47)
where |X| corresponds to a network invariant such as the total number of nodes or the total number of edges. The network is divided into n subsets, based on a given criterion, and the value |Xi| denotes the cardinality of subset i. A larger indicates a higher network-variation.
Here, as a simple example, we consider the graph entropy based on the node degree [47]. Let Nk be the number of nodes with degree k; the graph entropy is given as
(2.48)
Since Nk/N = P(k) (i.e., the degree distribution), this equation is rewritten as . That is, the graph entropy characterizes the degree of heterogeneity in a network. For example, random (homogeneous) networks and scale-free (heterogeneous) networks have a high and a low , respectively.
Furthermore, we can define several graph entropies, based on different criteria, and these graph entropies are applied in a wide range of research fields (see Ref. [45] for details).
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