Mareike Bockholt

(Algorithm Accountability Lab)
hosted by PhD Program in CS @ TU KL

"Towards a process-driven network analysis"

In the recent decades, there has been in an increasing interest in analyzing the behaviour of complex systems. A complex system consists of independent entities interacting with each other such that the system shows a so-called emergent behaviour, a behaviour which cannot be explained by the behaviour of the single entities, but only by their interactions. A popular approach for analyzing such systems is a network analytic view where the system is represented by a graph structure: nodes represent the system's entities, edges their interactions. A large toolbox of network analytic methods, such as measures for structural properties, centrality measures, methods for identifying communities, etc, is readily available to be applied on any network structure -- and one is tempted to do so. However, it is often overseen that a network representation of a system and the (technically applicable) methods contain assumptions which need to be met, otherwise the results are not interpretable or even misleading. The most important assumption of any network representation is the presence of indirect effect: if A has an impact on B, and B has an impact on C, a network representation assumes that also A has an impact on C. If such indirect effects are not present in the system, a network representation is meaningless. A presence of indirect effects however implies that "something" is flowing through the network. Otherwise indirect effects are not explicable. Those network flows (we also call them network processes) can be the propagation of information in social networks, the spreading of infections, but also entities using the network as infrastructure as in transportation networks. For a meaningful network analysis, the network process, the network representation and the network measures cannot be chosen independently [Dorn2012]: the network representation and the network process need to match (investigating how an infection might spread by using an online social network as Facebook is pointless), and the network method and the network process need to match (applying a measure assuming that the process uses shortest paths is pointless for the process of information spreading) [Borgatti2005].We claim that the network process dictates the suitable network representations and the suitable network methods and call this approach "process-driven network analysis". In order to show the necessity of this approach, we use four data sets of real-world processes. In this work, as first step, we show that the assumptions of standard network measures about the properties of a network process are not fulfilled by the real-world process data. As second step, we compare the network usage pattern by real-world processes to the usage pattern of the corresponding shortest paths and random walks.

Time: Monday, 02.12.2019, 15:30
Place: 48-680