As one example of critical infrastructures, the German high-speed train network (ICE) is a prime target for terrorism. To decrease the impact of attacks, key stations need to be identified as the most likely targets. One approach for doing so is modeling the network as a graph and then applying suitable graph measures to it. The central concern of this thesis is the fact that there is a large number of old and new measures, which all provide their unique perspective, but which eventually lead to an information overload for the decision-makers. The solution presented takes the "Technique for Order Preference by Similarity to Ideal Solution" (TOPSIS) from Multi-criteria Decision Making field (MCDM) and adapts it to produce a new aggregation framework of different graph measures. For the vital step during this process, a novel, mathematical methodology is being presented, replacing the traditional expert knowledge needed. Furthermore, to verify the effectiveness of the aggregation measure compared to other graph measures, a new network performance metric is being introduced and validated. As an outlook, a special vector-based approach based on the obtained results is addressed.    «

As one example of critical infrastructures, the German high-speed train network (ICE) is a prime target for terrorism. To decrease the impact of attacks, key stations need to be identified as the most likely targets. One approach for doing so is modeling the network as a graph and then applying suitable graph measures to it. The central concern of this thesis is the fact that there is a large number of old and new measures, which all provide their unique perspective, but which eventually lead to...    »