论文标题
在定向网络中订购的社区检测
Ordered community detection in directed networks
论文作者
论文摘要
我们开发了一种推断有指示网络中社区结构的方法，在该网络中以潜在的一维层次结构排序，该层次结构决定了首选边缘方向。我们的非参数贝叶斯方法基于随机块模型（SBM）的修改，该方法可以利用等级对齐和连贯性，以产生将有序的层次结合与组之间任意混合模式相结合的网络的拼写描述。由于我们的模型还包括定向度校正，因此我们可以使用它来区分非本地分层结构与局部内和级别的不平衡 - 从而消除了大多数排名方法中存在的汇合源。我们还展示了如何可靠地与无序的SBM变体获得的结果可靠地比较，以确定层次排序是否首先是统计上的。我们说明了我们的方法在几个领域的各种经验网络上的应用。
We develop a method to infer community structure in directed networks where the groups are ordered in a latent one-dimensional hierarchy that determines the preferred edge direction. Our nonparametric Bayesian approach is based on a modification of the stochastic block model (SBM), which can take advantage of rank alignment and coherence to produce parsimonious descriptions of networks that combine ordered hierarchies with arbitrary mixing patterns between groups. Since our model also includes directed degree correction, we can use it to distinguish non-local hierarchical structure from local in- and out-degree imbalance -- thus removing a source of conflation present in most ranking methods. We also demonstrate how we can reliably compare with the results obtained with the unordered SBM variant to determine whether a hierarchical ordering is statistically warranted in the first place. We illustrate the application of our method on a wide variety of empirical networks across several domains.
