论文标题
社交网络中的相关距离
Correlation distances in social networks
论文作者
论文摘要
在这项工作中，我们探讨了复杂网络中的度量分类性，并将其通常的定义扩展到最近的邻居的定义之外。我们将此定义应用于模型网络，并描述一种诱导各种性能的重新布线算法。我们将这些结果与真实网络进行比较。与许多其他类型的复杂网络相比，社交网络尤其倾向于将其与程度混合在一起。但是，我们在这里表明，在一步之后以及在所分析的大多数经验网络中，这些正相关减少了。除了学位之外，属性支持这一点，例如科学合着者网络中的论文数量，最近的邻居以外没有相关性。除了下一个最近的邻居以外，我们还观察到节点的腹膜内态趋势三步之遥，这表明该距离的节点与类似的可能性更大。
In this work we explore degree assortativity in complex networks, and extend its usual definition beyond that of nearest neighbours. We apply this definition to model networks, and describe a rewiring algorithm that induces assortativity. We compare these results to real networks. Social networks in particular tend to be assortatively mixed by degree in contrast to many other types of complex networks. However, we show here that these positive correlations diminish after one step and in most of the empirical networks analysed. Properties besides degree support this, such as the number of papers in scientific coauthorship networks, with no correlations beyond nearest neighbours. Beyond next-nearest neighbours we also observe a diasassortative tendency for nodes three steps away indicating that nodes at that distance are more likely different than similar.
