基于标签传播的节点属性与网络结构一致性测试
Testing Alignment of Node Attributes with Network Structure Through Label Propagation
摘要：
属性网络数据在各个领域中变得越来越普遍，因为我们通常除了节点的成对连接模式之外还配备了关于节点的信息。这些额外的信息可以表现为分类，也可以表现为多维特征向量。最近开发的寻求将社区发现方法扩展到属性网络的方法已经探索了如何最有效地结合连接性和属性信息来识别质量社区。这些方法通常依赖于对属性和连通性之间的依赖关系的一些假设。在这项工作中，我们试图开发一个统计测试，以评估节点属性是否与网络连接。我们的目标是定量评估是否具有类似的连接模式的节点也有类似的属性。为了解决这个问题，我们使用节点采样和标签传播方法。我们将我们的方法应用于几个合成的例子，探讨网络结构和属性特征如何影响我们的方法计算的经验p值。最后，我们将该测试应用于从单细胞质谱仪（CyTOF）数据集生成的网络，并表明我们的测试可以识别与不同单细胞亚群相关的标记物。
英文摘要：
Attributed network data is becoming increasingly common across fields, as we are often equipped with information about nodes in addition to their pairwise connectivity patterns. This extra information can manifest as a classification, or as a multidimensional vector of features. Recently developed methods that seek to extend community detection approaches to attributed networks have explored how to most effectively combine connectivity and attribute information to identify quality communities. These methods often rely on some assumption of the dependency relationships between attributes and connectivity. In this work, we seek to develop a statistical test to assess whether node attributes align with network connectivity. The objective is to quantitatively evaluate whether nodes with similar connectivity patterns also have similar attributes. To address this problem, we use a node sampling and label propagation approach. We apply our method to several synthetic examples that explore how network structure and attribute characteristics affect the empirical p-value computed by our method. Finally, we apply the test to a network generated from a single cell mass cytometry (CyTOF) dataset and show that our test can identify markers associated with distinct sub populations of single cells.
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