A class of vertex–edge-growth small-world network models having scale-free, self-similar and hierarchical characters

Abstract The problem of uncovering the internal operating function of network models is intriguing, demanded and attractive in researches of complex networks. Notice that, in the past two decades, a great number of artificial models are built to try to answer the above mentioned task. Based on the different growth ways, these previous models can be divided into two categories, one type, possessing the preferential attachment, follows a power-law P ( k ) ∼ k − γ , 2 γ 3 . The other has exponential-scaling feature, P ( k ) ∼ α − k . However, there are no models containing above two kinds of growth ways to be presented, even the study of interconnection between these two growth manners in the same model is lacking. Hence, in this paper, we construct a class of planar and self-similar graphs motivated from a new attachment way, vertex–edge-growth network-operation, more precisely, the couple of both them. We report that this model is sparse, small world and hierarchical. And then, not only is scale-free feature in our model, but also lies the degree parameter γ ( ≈ 3 . 242 ) out the typical range. Note that, we suggest that the coexistence of multiple vertex growth ways will have a prominent effect on the power-law parameter γ , and the preferential attachment plays a dominate role on the development of networks over time. At the end of this paper, we obtain an exact analytical expression for the total number of spanning trees of models and also capture spanning trees entropy which we have compared with those of their corresponding component elements.