Node-vector-distance calculation in edge-weighted networks

When the CoVid pandemic erupted in China, as serious and infectious as it proved to be, most people around the world did not consider it an emergency because they perceived China as being "far away" from them and thus, not a threat to their well-being. However, what people failed to realize is that despite the physical distance between themselves and China, the country is only a flight away from any place in the world. Although physical distance is much less of a meaningful barrier nowadays than it was in the past, our approach to measuring and predicting the global spread of phenomena such as information, relationships, cultural shifts and even disease is often based in the outdated perception that physical proximity is much more relevant than it actually is. This thesis work aims to prove that, instead of using physical distance to analyze and predict global spread, complex networks are a much more suitable technique because they illustrate the meaningful nodes and connections that determine the trajectory of the spread. To demonstrate this, CoVid 19 was used as a case study. In order to be able to compare the networks, a noise-corrected network backboning method was applied to reduce the largest network. For the modeling of the spread of the disease, the Euclidean The generalized method was applied in order to calculate the node-vector distance at subsequent time steps of the disease. As a result of this the study, two novel datasets corresponding to country-to-country proximity and country-to-country flights were generated, and the resulting Noise Corrected flight network was shown to more accurately model the spread of CoVid-19 in this particular instance