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

TL;DR

This paper investigates how different normalization and aggregation choices significantly affect node centrality rankings in multiplex networks, revealing high sensitivity and potential misinterpretations in network analysis.

Contribution

It demonstrates the impact of modeling decisions on centrality rankings and introduces a new visualization method for node sensitivity analysis in multiplex networks.

Findings

Node rankings vary greatly with different normalization and aggregation strategies.

High sensitivity of individual nodes to preprocessing choices impacts interpretation.

Simple centrality measures are highly affected by modeling decisions.

Abstract

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.