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.
Beschreibung
[1606.05468] Most central or least central? How much modeling decisions influence a node's centrality ranking in multiplex networks
%0 Generic
%1 tavassoli2016central
%A Tavassoli, Sude
%A Zweig, Katharina Anna
%D 2016
%K centrality complex multiplex networks
%T Most central or least central? How much modeling decisions influence a
node's centrality ranking in multiplex networks
%U http://arxiv.org/abs/1606.05468
%X 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.
@misc{tavassoli2016central,
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.},
added-at = {2016-09-23T10:16:20.000+0200},
author = {Tavassoli, Sude and Zweig, Katharina Anna},
biburl = {https://www.bibsonomy.org/bibtex/21ace5b52d43ed4f2ab096d2189568dec/mbockholt},
description = {[1606.05468] Most central or least central? How much modeling decisions influence a node's centrality ranking in multiplex networks},
interhash = {529a138f855edd51111a7abb5f4b6076},
intrahash = {1ace5b52d43ed4f2ab096d2189568dec},
keywords = {centrality complex multiplex networks},
note = {cite arxiv:1606.05468},
timestamp = {2016-09-23T10:16:20.000+0200},
title = {Most central or least central? How much modeling decisions influence a
node's centrality ranking in multiplex networks},
url = {http://arxiv.org/abs/1606.05468},
year = 2016
}