Clusters or communities can provide a coarse-grained description of complex systems at multiple scales, but their detection remains challenging in practice. Community detection methods often define communities as dense subgraphs, or subgraphs with few connections in-between, via concepts such as the cut, conductance, or modularity. Here we consider another perspective built on the notion of local dominance, where low-degree nodes are assigned to the basin of influence of high-degree nodes, and design an efficient algorithm based on local information. Local dominance gives rises to community centers, and uncovers local hierarchies in the network. Community centers have a larger degree than their neighbors and are sufficiently distant from other centers. The strength of our framework is demonstrated on synthesized and empirical networks with ground-truth community labels. The notion of local dominance and the associated asymmetric relations between nodes are not restricted to community detection, and can be utilised in clustering problems, as we illustrate on networks derived from vector data.

翻译：聚类或社群能够提供多尺度复杂系统的粗粒度描述，但其检测在实践中仍具挑战性。社群检测方法通常通过切割、传导度或模块度等概念，将社群定义为稠密子图或内部连接稀疏的子图。本文基于局部优势概念提出另一种视角——将低度节点分配给高度节点的势力范围，并设计了一种基于局部信息的高效算法。局部优势催生社群中心，揭示网络中的局部层级结构。社群中心的度高于其邻居节点，且与其他中心保持足够距离。该框架的有效性在具有真实社群标签的合成网络与实证网络中得到验证。局部优势概念及其关联的节点间非对称关系不仅限于社群检测，还可应用于聚类问题——我们在基于向量数据导出的网络中对此进行了验证。