As a fundamental task in cluster analysis, community detection is crucial for the understanding of complex network systems in many disciplines such as biology and sociology. Recently, due to the increase in the richness and variety of attribute information associated with individual nodes, detecting communities in attributed graphs becomes a more challenging problem. Most existing works focus on the similarity between pairwise nodes in terms of both structural and attribute information while ignoring the higher-order patterns involving more than two nodes. In this paper, we explore the possibility of making use of higher-order information in attributed graphs to detect communities. To do so, we first compose tensors to specifically model the higher-order patterns of interest from the aspects of network structures and node attributes, and then propose a novel algorithm to capture these patterns for community detection. Extensive experiments on several real-world datasets with varying sizes and different characteristics of attribute information demonstrated the promising performance of our algorithm.

中文翻译：

---

利用高阶模式在属性图中进行社区检测

作为聚类分析的一项基本任务，社区检测对于理解生物学和社会学等许多学科中的复杂网络系统至关重要。近来，由于与各个节点相关联的属性信息的丰富性和多样性的增加，在属性图中检测社区变得更具挑战性。现有的大多数工作着眼于结构和属性信息方面的成对节点之间的相似性，而忽略了涉及两个以上节点的高阶模式。在本文中，我们探索了利用归因图中的高阶信息来检测社区的可能性。为此，我们首先组成张量以从网络结构和节点属性等方面专门对感兴趣的高阶模式进行建模，然后提出一种新颖的算法来捕获这些模式以进行社区检测。在具有可变大小和属性信息不同特征的几个真实世界数据集上的大量实验证明了我们算法的有希望的性能。