Table 3 Classical random network models against which topological characteristics of real-world networks are often compared

The oldest class of random networks. To construct a graph instance, links are added between each pair of nodes with probability p (a parameter).

A kind of generalization of ER networks in which links of a regular lattice are rewired. Characterized by high clustering coefficients and short average path lengths.

A class of random networks constructed one node at a time, with new nodes preferentially attaching to existing high-degree nodes. These networks are scale-free (i.e. hub-like) and more closely resemble molecular interaction network networks than ER or WS networks.

These networks, inspired by gene duplication and subsequent divergence (in sequence, interaction and function) [38] are generated by duplicating nodes and randomly removing/adding links. Architecturally, duplication-divergence networks are similar to Barabási-Albert networks [39, 40]

Random networks characterized by their specific node degree sequences that are generated either by randomly rewiring the links of an existing network [41] or through the configuration model [42, 43].