Most central or least central? How much modeling decisions influence a node's centrality ranking in multiplex networks
This highlights a critical, often overlooked issue in network analysis that challenges the reliability of interpretations in intelligence-analytic software and similar applications.
The study demonstrates that different normalization and aggregation strategies in multiplex networks can drastically alter a node's centrality ranking, turning it from most to least central, using three multiplex networks as examples.
To understand a node's centrality in a multiplex network, its centrality values in all the layers of the network can be aggregated. This requires a normalization of the values, to allow their meaningful comparison and aggregation over networks with different sizes and orders. The concrete choices of such preprocessing steps like normalization and aggregation are almost never discussed in network analytic papers. In this paper, we show that even sticking to the most simple centrality index (the degree) but using different, classic choices of normalization and aggregation strategies, can turn a node from being among the most central to being among the least central. We present our results by using an aggregation operator which scales between different, classic aggregation strategies based on three multiplex networks. We also introduce a new visualization and characterization of a node's sensitivity to the choice of a normalization and aggregation strategy in multiplex networks. The observed high sensitivity of single nodes to the specific choice of aggregation and normalization strategies is of strong importance, especially for all kinds of intelligence-analytic software as it questions the interpretations of the findings.