Community detection plays a pivotal role in uncovering closely connected subgraphs, aiding various real-world applications such as recommendation systems and anomaly detection. With the surge of rich information available for entities in real-world networks, the community detection problem in attributed networks has attracted widespread attention. While previous research has effectively leveraged network topology and attribute information for attributed community detection, these methods overlook two critical issues: (i) the semantic similarity between node attributes within the community, and (ii) the inherent mesoscopic structure, which differs from the pairwise connections of the micro-structure. To address these limitations, we propose HACD, a novel attributed community detection model based on heterogeneous graph attention networks. HACD treats node attributes as another type of node, constructs attributed networks into heterogeneous graph structures and employs attribute-level attention mechanisms to capture semantic similarity. Furthermore, HACD introduces a community membership function to explore mesoscopic community structures, enhancing the robustness of detected communities. Extensive experiments demonstrate the effectiveness and efficiency of HACD, outperforming state-of-the-art methods in attributed community detection tasks. Our code is publicly available at https://github.com/Anniran1/HACD1-wsdm.

翻译：社区发现在揭示紧密连接的子图方面发挥着关键作用，有助于推荐系统和异常检测等多种现实应用。随着现实世界网络中实体可用信息的激增，属性网络中的社区发现问题引起了广泛关注。尽管先前的研究已有效利用网络拓扑和属性信息进行属性社区发现，但这些方法忽略了两个关键问题：（i）社区内节点属性之间的语义相似性，以及（ii）不同于微观结构成对连接的固有介观结构。为应对这些局限，我们提出了HACD，一种基于异构图注意力网络的新型属性社区发现模型。HACD将节点属性视为另一类节点，将属性网络构建为异构图结构，并采用属性级注意力机制来捕获语义相似性。此外，HACD引入了社区隶属度函数以探索介观社区结构，从而增强所发现社区的鲁棒性。大量实验证明了HACD的有效性和高效性，其在属性社区发现任务中优于现有最先进方法。我们的代码公开于https://github.com/Anniran1/HACD1-wsdm。