论文标题
关于在网络中评估同质性的名义分类性不足
On the inadequacy of nominal assortativity for assessing homophily in networks
论文作者
论文摘要
名义分类性（或离散的分类性）被广泛用于表征群体混合模式和网络中均匀的均匀性，使研究人员能够分析群体如何彼此相互作用。在这里，我们证明，当应用于具有不平等群体大小和不对称混合的网络时，该度量会带来严重的缺点。我们通过分析表征这些缺点，并使用合成和经验网络来表明名义分类性无法解释群体不平衡和不对称组相互作用，从而导致混合模式的表征不准确。我们提出了调整后的名义分类性，并表明此调整可恢复具有各种混合水平的网络中的预期分类性。此外，我们提出了一种分析方法，通过估计组间和组内连接率的趋势来评估不对称混合。最后，我们讨论了这种方法如何使现实世界网络中的隐藏混合模式。
Nominal assortativity (or discrete assortativity) is widely used to characterize group mixing patterns and homophily in networks, enabling researchers to analyze how groups interact with one another. Here we demonstrate that the measure presents severe shortcomings when applied to networks with unequal group sizes and asymmetric mixing. We characterize these shortcomings analytically and use synthetic and empirical networks to show that nominal assortativity fails to account for group imbalance and asymmetric group interactions, thereby producing an inaccurate characterization of mixing patterns. We propose adjusted nominal assortativity and show that this adjustment recovers the expected assortativity in networks with various level of mixing. Furthermore, we propose an analytical method to assess asymmetric mixing by estimating the tendency of inter- and intra-group connectivities. Finally, we discuss how this approach enables uncovering hidden mixing patterns in real-world networks.
