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首页> 外文会议>European Network Intelligence Conference >Most Central or Least Central? How Much Modeling Decisions Influence a Node's Centrality Ranking in Multiplex Networks
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Most Central or Least Central? How Much Modeling Decisions Influence a Node's Centrality Ranking in Multiplex Networks

机译：最中央还是最小中央？多少个建模决策会影响多路复用网络中节点的中心度排名

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摘要

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.
机译：为了了解一个节点在Multiplex网络中的中心性，可以汇总其在网络所有层中的中心性值。这需要对值进行规范化，以允许它们在具有不同大小和顺序的网络上进行有意义的比较和汇总。网络分析论文几乎从未讨论过像标准化和聚合这样的预处理步骤的具体选择。在本文中，我们表明，即使坚持最简单的中心性指数（程度），但使用标准化和聚合策略的不同经典选择也可以使节点从最中心的位置转变为最不中心的位置。我们通过使用聚合算子来展示我们的结果，该算子在基于三个复用网络的不同经典聚合策略之间进行缩放。我们还为节点对多路复用网络中标准化和聚合策略选择的敏感性进行了新的可视化和表征。观察到的单个节点对聚合和归一化策略的特定选择的高度敏感性非常重要，特别是对于所有类型的智能分析软件，因为它对结果的解释提出了质疑。

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