Complex networks are systems of entities that are interconnected through meaningful relationships, resulting in structures that have statistical complexities not formed by random chance. Many graph model algorithms have been proposed to model the observed behaviours of complex networks. However, constructing such graph models manually is both tedious and problematic. Moreover, many of the models proposed in the literature have been cited as having inaccuracies with respect to the complex networks they represent. Although recent studies have proposed using genetic programming to automate the construction of graph model algorithms, only one such study has considered directed networks. This paper proposes a GP-based inference system that automatically constructs graph models for directed complex networks. Furthermore, the system proposed in this paper facilitates the use of vertex attributes, e.g., age, to incorporate network semantics — something which previous works lack. The GP system was used to reproduce three well-known graph models. Results indicate that the networks generated by the (automatically) constructed models were structurally similar to networks generated by their respective target models.
['Michael Richard Medland', 'Kyle Robert Harrison', 'Beatrice M. Ombuki-Berman']
Automatic inference of graph models for directed complex networks using genetic programming