Generative network models

Real-world networked systems arise by the gradual addition of elements attaching to an existing and evolving network component. Networks consist of multiple inter-related elements displaying a collective behavior that generally arises from temporary, short-range, nonlinear interactions. With our modern access to data, the study of networks offers a structuralist approach that facilitates the use of an extensive set of mathematical techniques for visualizing and measuring different types of interactions in social and engineering systems. This research proposes a generative mechanism for constructing, through a process of distributed decision-making, substrates for the study of collective dynamics on power-law weighted networks with different types of links, and both a desired scaling exponent and clustering coefficient. The purpose of this research is twofold: first, to identify a wide class of attachment strategies that lead to distinct structural features often seen in real-word networks; second, to apply the proposed framework to generate network substrates that resemble the network of patents granted by the Colombian Superintendencia de Industria y Comercio.