Abstract
A popular approach for understanding complex systems is a network analytic one: the system’s entities and their interactions are represented by a graph structure such that readily available methods suitable for graph structures can be applied. A network representation of a system enables the analysis of indirect effects: if A has an impact on B, and B has an impact on C, then, A also has an impact on C. This is often due to some kind of process flowing through the network, for example, pieces of informations or viral infections in social systems, passenger flows in transportation systems, or traded goods in economic systems. We argue that taking into account the actual usage of the system additionally to the static network representation of the system can yield interesting insights: first, the network representation and applicable network methods cannot be chosen independently from the network process of interest (Borgatti 2005; Dorn et al. 2012; Zweig 2016; Butts 2009). Therefore, focussing on the relevant network process in an early stage of the research project helps to determine suitable network representations and methods in order to obtain meaningful results (we call this approach process-driven network analysis). Second, many network methods assume that the spreading of some entity follows shortest or random paths. However, we show that not all flows are well approximated by this. In these cases, incorporating the network usage creates a real addition of knowledge to the static aggregated network representation.NoteThis is an extended and revised version of a conference article (Bockholt and Zweig 2019), published and presented at COMPLEX NETWORKS 2019.
Highlights
In the past two decades, the interest in complex systems has risen tremendously
The results presented in the previous sections showed that real-world processes use the network in a different way than the implicit process model used for the computation of standard network measures, such as centrality measures: there are a few nodes and node pairs between which a large proportion of the real flow takes place, while there is almost no flow between most node pairs
The usage of the nodes by real and random walks show a high correlation for three of the four data sets. We demonstrated that this does have an effect on network measures, demonstrated for standard centrality measures in “Effect on network measures” section. Those results have relevance for network analysis in general because it shows that real-world processes show different characteristics than predicted by the simple process models implicitly contained in network measures
Summary
In the past two decades, the interest in complex systems has risen tremendously. Examples of complex systems include social systems of humans, biological systems of protein-protein-interactions, or transportation systems as the world-wide air transportation system. “Why network flows need to be considered” shows that real-worldnetwork flows contained in our data sets do not show the property of an equal amount of flow between each node pair This demonstrates that considering the processes taking place in the system creates added value to the analysis of the static aggregated network representation. In a previous work (Bockholt and Zweig 2018), we considered the betweenness centrality and introduced process-based betweenness measure variants: these partly use the process model contained in the original betweenness centrality and partly use information about the behaviour of real-world network flows contained in empiric data sets. Most of these source-target pairs are only used rarely or not at all by the real-world network flow, which is why the high importance of the node Anchorage is not supported by the empiric betweenness
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
