Why We Need a Process-Driven Network Analysis

Abstract

A network representation is a powerful abstraction of a complex system, on which a full range of readily available methods from network analysis can be applied. A network representation is suitable if indirect effects are of interest: if A has an impact on B and B has an impact on C, it is assumed that also A has an impact on C. This implies that some process is flowing through the network. For a meaningful network analysis, the network process, the network representation, and the applied network measure cannot be chosen independently [3, 4, 9, 30]. We propose a process-driven perspective on network analysis, which takes into account the network process additionally to the network representation. In order to show the necessity of this approach, we collected four data sets of real-world processes. 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. Our results support the importance of a process-driven network analysis.