论文标题
本地优势揭示了网络中的群集
Local dominance unveils clusters in networks
论文作者
论文摘要
集群或社区可以在多个尺度上提供对复杂系统的粗粒描述，但是在实践中，它们的检测仍然具有挑战性。社区检测方法通常将社区定义为密集的子图，或通过诸如剪切，电导或模块化之类的概念之间的连接范围很少的子图。在这里，我们考虑了另一个观点，基于局部优势的概念，在该概念上，低度节点被分配给高度节点影响的盆地，并根据本地信息设计有效的算法。当地的主导地位使社区中心增加了，并揭示了网络中的本地层次结构。社区中心的学位比邻居更大，并且与其他中心相距甚远。我们的框架的强度在具有地面社区标签的合成和经验网络上得到了证明。正如我们在向量数据中得出的网络上所说明的那样，局部优势和节点之间相关的不对称关系的概念不仅限于社区检测。
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.
