Attributed graph (AG) clustering is a fundamental, yet challenging, task for studying underlying network structures. Recently, a variety of graph representation learning models has been proposed to effectively infer the node embeddings, which are then incorporated into conventional clustering techniques to identify meaningful clusters. While these models tend to preserve node proximities, which reflect the similarity between nodes in both structural and attribute dimensions, for representation learning, they generally overlook the crucial dependencies between node embeddings and the resulting clusters. To overcome this problem, we propose a novel fuzzy-based deep AG clustering model, namely FDAGC, which is capable of achieving the task in a purely unsupervised and end-to-end manner without additionally incorporating conventional clustering techniques. In particular, FDAGC first encodes network structures and node attributes into a compact representation with graph convolution. A reconstruction error is then estimated to minimize the information loss during network message-passing. Besides, we utilize a self-monitoring training strategy to optimize node embeddings, thus improving the cluster cohesion by guiding them toward cluster centers. In the training phase, our expectations about resulting clusters are explicitly incorporated into the optimization of FDAGC via the concept of fuzzy clustering, thus leading to more accurate clustering by coupling the dependency between graph representation learning and AG clustering. Extensive experiments have demonstrated the superior performance of FDAGC in terms of several evaluation metrics, such as accuracy, normalized mutual information, F1-score and adjusted rand index, on six real-world AGs with different scales.

中文翻译：

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使用 OWA 算子生成模糊测量的熔化概率测量：新月法


属性图（AG）聚类是研究底层网络结构的一项基本但具有挑战性的任务。最近，人们提出了各种图表示学习模型来有效地推断节点嵌入，然后将其合并到传统的聚类技术中以识别有意义的聚类。虽然这些模型倾向于保留节点邻近性，这反映了节点之间在结构和属性维度上的相似性，但对于表示学习，它们通常忽略了节点嵌入和生成的集群之间的关键依赖关系。为了克服这个问题，我们提出了一种新颖的基于模糊的深度 AG 聚类模型，即 FDAGC，它能够以纯粹无监督和端到端的方式完成任务，而无需额外结合传统的聚类技术。特别是，FDAGC 首先使用图卷积将网络结构和节点属性编码为紧凑表示。然后估计重建误差以最小化网络消息传递期间的信息丢失。此外，我们利用自我监控训练策略来优化节点嵌入，从而通过引导它们走向聚类中心来提高聚类凝聚力。在训练阶段，我们对结果聚类的期望通过模糊聚类的概念明确地纳入 FDAGC 的优化中，从而通过耦合图表示学习和 AG 聚类之间的依赖关系来实现更准确的聚类。大量实验证明 FDAGC 在六个不同规模的现实世界 AG 上在准确性、归一化互信息、F1 分数和调整兰德指数等多个评估指标方面具有优越的性能。