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To understand a node's centrality in a multiplex network, its centralityvalues in all the layers of the network can be aggregated. This requires anormalization of the values, to allow their meaningful comparison andaggregation over networks with different sizes and orders. The concrete choicesof such preprocessing steps like normalization and aggregation are almost neverdiscussed in network analytic papers. In this paper, we show that even stickingto the most simple centrality index (the degree) but using different, classicchoices of normalization and aggregation strategies, can turn a node from beingamong the most central to being among the least central. We present our resultsby using an aggregation operator which scales between different, classicaggregation strategies based on three multiplex networks. We also introduce anew visualization and characterization of a node's sensitivity to the choice ofa normalization and aggregation strategy in multiplex networks. The observedhigh sensitivity of single nodes to the specific choice of aggregation andnormalization strategies is of strong importance, especially for all kinds ofintelligence-analytic software as it questions the interpretations of thefindings.

Most central or least central? How much modeling decisions influence a node's centrality ranking in multiplex networks