Random Reference Models and Network Rewiring in Temporal Network Clustering

Computing on temporal networks is difficult because of their dynamic nature. One way to solve this is to slice them into multilayer networks, but this results in a loss of information. This thesis tries to find out at which number of slices this loss of information is at a minimum by using random reference models, algorithms that randomize a specific part of the network, and community detection to extract the impact of the slicing. This is done by calculating modularity, how strongly connected the communities are, before and after randomization. For three of the four datasetsthat were tested a maximum was found where a larger part of the network's community structure was destroyed and thus a smaller part connected to the conversion from a temporal network to a multilayer network. The method tested could be used for some networks to find when the loss of information is at its lowest, but further experiments are required to prove to which networks the technique can be applied