Abstract

Abstract: Many of the algorithms used for community detection in temporal networks have been adapted from static network theory. A common approach in dealing with the temporal dimension is to create multiple static networks from one temporal, based on a time condition. In this thesis, focus lies on identifying the optimal partitioning of a few temporal networks. This is done by utilizing the popular community detection algorithm called Generalized Louvain. Output of the Generalized Louvain comes in two parts. First, the created community structure, i.e. emph{how} the network is connected. Secondly, a measure called modularity, which is a scalar value representing the quality of the identified community structure. The methodology used is aimed at creating a comparable result by normalizing modularity. The normalization process can be explained in two major steps: 1) study the effects on modularity when partitioning a temporal network in an increasing number of slices. 2) study the effects on modularity when varying the number of connections (edges) in each time slice. The results show that the created methodology yields comparable results on two out of the four here tested temporal networks, implying that it might be more suited for some networks than others. This can serve as an indication that there does not exist a general model for community detection in temporal networks. Instead, the type of network is key to choosing the method.