The joint use of node features and network topology to detect communities is called community detection in attributed networks. Most of the existing work along this line has been carried out through objective function optimization and has proposed numerous approaches. However, they tend to focus only on lower-order details, i.e., capture node features and network topology from node and edge views, and purely seek a higher degree of optimization to guarantee the quality of the found communities, which exacerbates unbalanced communities and free-rider effect. To further clarify and reveal the intrinsic nature of networks, we conduct triangle-oriented community detection considering node features and network topology. Specifically, we first introduce a triangle-based quality metric to preserve higher-order details of node features and network topology, and then formulate so-called two-level constraints to encode lower-order details of node features and network topology. Finally, we develop a local search framework based on optimizing our objective function consisting of the proposed quality metric and two-level constraints to achieve both non-overlapping and overlapping community detection in attributed networks. Extensive experiments demonstrate the effectiveness and efficiency of our framework and its potential in alleviating unbalanced communities and free-rider effect.

联合使用节点特征和网络拓扑来检测社区被称为属性网络中的社区检测。大多数现有的这方面的工作都是通过目标函数优化来进行的，并提出了许多方法。然而，他们往往只关注低阶细节，即从节点和边缘视图捕获节点特征和网络拓扑，纯粹寻求更高程度的优化来保证发现的社区质量，这加剧了社区和自由的不平衡。 - 骑手效应。为了进一步阐明和揭示网络的本质，我们考虑节点特征和网络拓扑进行面向三角形的社区检测。具体来说，我们首先引入基于三角形的质量度量来保留节点特征和网络拓扑的高阶细节，然后制定所谓的两级约束来编码节点特征和网络拓扑的低阶细节。最后，我们开发了一个基于优化目标函数的本地搜索框架，该目标函数由所提出的质量度量和两级约束组成，以实现属性网络中的非重叠和重叠社区检测。大量的实验证明了我们框架的有效性和效率，及其在缓解社区不平衡和搭便车效应方面的潜力。