A compendium of applications of tailor-made network-theoretic tools have been devised and implemented in a data-driven fashion. In the first part, a (formerly) novel centrality metric, aptly named “bridgeness”, based on a decomposition of the standard betweenness centrality, will be introduced. A prominent feature is its agnosticism with regard to any possible community structure prior. A second application is aimed at describing dynamic features of temporal graphs which are apparent at the mesoscopic level. A dataset comprising 40 years' worth of selected scientific publications is used to highlight the appearance and evolution in time of a specific field of study: “wavelets”. Persistent features are discriminated from transient artifacts, taking advantage of a “laminar stream” concept, on which the “complexity score” we seek to optimize is based. In a similar vein, a network of Japanese businesses, based on a dataset which includes (indirect) information on co-owned overseas subsidiaries, is presented. A hotly debated issue in the field of industrial economics, the Miwa-Ramseyer hypothesis, is been conclusively shown to be false, at least in its weak form.
Finally, an augmented version of Schelling's classical segregation model is presented, which includes an important sociological ingredient: the evolution of individual characteristics. A simple co-evolution mechanism of micro and macro levels is thus introduced: more specifically, we add a feedback mechanism linking the agents' interactions and their individual characteristics.