Attribute-aware community discovery aims to find well-connected communities that are also homogeneous w.r.t. the labels carried by the nodes. In this work, we address such a challenging task presenting Eva, an algorithmic approach designed to maximize a quality function tailoring both structural and homophilic clustering criteria. We evaluate Eva on several real-world labeled networks carrying both nominal and ordinal information, and we compare our approach to other classic and attribute-aware algorithms. Our results suggest that Eva is the only method, among the compared ones, able to discover homogeneous clusters without considerably degrading partition modularity.We also investigate two well-defined applicative scenarios to characterize better Eva: i) the clustering of a mental lexicon, i.e., a linguistic network modeling human semantic memory, and (ii) the node label prediction task, namely the problem of inferring the missing label of a node.

中文翻译：

---

识别和利用标记网络中的同质社区

感知属性的社区发现旨在找到连接良好的社区，这些社区对于节点所携带的标签也是同质的。在这项工作中，我们通过Eva解决了这一具有挑战性的任务，Eva是一种算法方法，旨在最大程度地调整剪裁结构聚类和同质聚类标准的质量函数。我们在带有标称和序号信息的几个真实标签网络上评估Eva，并将我们的方法与其他经典和可识别属性的算法进行比较。我们的结果表明，在比较的方法中，Eva是唯一能够发现同质集群而不会显着降低分区模块性的方法。我们还研究了两种定义明确的应用场景以更好地表征Eva：i）心理词典的聚类，即对人类语义记忆建模的语言网络，以及（ii）节点标签预测任务，即推断节点缺失标签的问题。