Stella Massimo, De Domenico Manlio
Fondazione Bruno Kessler, Via Sommarive 18, 38123 Povo, Italy.
Entropy (Basel). 2018 Apr 11;20(4):268. doi: 10.3390/e20040268.
We introduce distance entropy as a measure of homogeneity in the distribution of path lengths between a given node and its neighbours in a complex network. Distance entropy defines a new centrality measure whose properties are investigated for a variety of synthetic network models. By coupling distance entropy information with closeness centrality, we introduce a network cartography which allows one to reduce the degeneracy of ranking based on closeness alone. We apply this methodology to the empirical multiplex lexical network encoding the linguistic relationships known to English speaking toddlers. We show that the distance entropy cartography better predicts how children learn words compared to closeness centrality. Our results highlight the importance of distance entropy for gaining insights from distance patterns in complex networks.
我们引入距离熵，作为衡量复杂网络中给定节点与其邻居之间路径长度分布均匀性的一种度量。距离熵定义了一种新的中心性度量，并针对各种合成网络模型研究了其性质。通过将距离熵信息与接近中心性相结合，我们引入了一种网络制图法，它能减少仅基于接近中心性进行排名时的退化性。我们将这种方法应用于编码说英语幼儿已知语言关系的实证多重词汇网络。我们表明，与接近中心性相比，距离熵制图法能更好地预测儿童学习单词的方式。我们的结果突出了距离熵对于从复杂网络中的距离模式获取见解的重要性。
