A simple and generic paradigm for creating complex networks using the strategy of vertex selecting-and-pairing

In many networks, the link between any pair of vertices conforms to a specific probability, such as the link probability proposed in the Barabási–Albert scale-free model. Here, we demonstrate how distributions of link probabilities can be utilized to generate various complex networks simply and effectively. In particular, we focus on the problem of complex network generation and develop a straightforward paradigm using the strategy of vertex selecting-and-pairing to create complex networks that are more generic than those generated by other relevant approaches. Crucially, our paradigm is capable of generating various complex networks with varied degree distributions using different probabilities for selecting vertices; however, in contrast, other relevant approaches can only be used to generate a specific type of complex network. We demonstrate our paradigm using four synthetic Barabási–Albert scale-free networks, four synthetic Watts–Strogatz small-world networks, and a real email network with a known degree distribution