  Knowledge graphs play a central role for linking different data which leads
to multiple layers. Thus, they are widely used in big data integration,
especially for connecting data from different domains. Few studies have
investigated the questions how multiple layers within graphs impact methods and
algorithms developed for single-purpose networks, for example social networks.
This manuscript investigates the impact of multiple layers on centrality
measures compared to single-purpose graph. In particular, (a) we develop an
experimental environment to (b) evaluate two different centrality measures -
degree and betweenness centrality - on random graphs inspired by social network
analysis: small-world and scale-free networks. The presented approach (c) shows
that the graph structures and topology has a great impact on its robustness for
additional data stored. Although the experimental analysis of random graphs
allows us to make some basic observations we will (d) make suggestions for
additional research on particular graph structures that have a great impact on
the stability of networks.
