PhD dissertation abstract
The analysis of Complex Networks turn out to be a very promising field of research, testified by many research projects and works that span different fields. Until recently, those analysis have been usually focused on deeply characterize a single aspect of the system, therefore a study that considers many informative axes along with a network evolve is lacking.
In this Thesis, we propose a new multidimensional analysis that is able to inspect networks in the two most important dimensions of a system, namely space and time. In order to achieve this goal, we studied them singularly and investigated how the variation of the constituting parameters drives changes to the network behaviour as a whole.
By focusing on space dimension, we were able to characterize spatial alteration in terms of abstraction levels. We propose a novel algorithm that, by applying a fuzziness function, can reconstruct networks under different level of details. We call this analysis telescopic as it recalls the magnification and reduction process of the lens.
Through this line of research we have successfully verified that statistical indicators, that are frequently used in many complex networks researches, depends strongly on the granularity (i.e, the detail level) with which a system is described and on the class of networks considered. Continue reading