Detecting the overlapping and hierarchical community structure in complex networks
Detecting the overlapping and hierarchical community structure in complex networks
复制标题
DOI：
10.1088/1367-2630/11/3/033015
复制
发表时间：
2009-03-10
影响因子：
3.3
通讯作者：
Kertesz, Janos
中科院分区：
文献类型：
--
作者：
Lancichinetti, Andrea;Fortunato, Santo;Kertesz, Janos
自然界、社会和技术中的许多网络都具有中观层次的组织结构，由一组节点组成紧密相连的单元，称为社区或模块，这些单元彼此之间只有很弱的联系。揭示这种社区结构是复杂网络领域最重要的问题之一。网络通常表现为分层组织，社区嵌入在其他社区中；此外，节点可以在不同的社区之间共享。在这里，我们提出了第一个既能找到重叠社区又能找到层次结构的算法。该方法基于适应度函数的局部最优。群落结构通过适应度直方图中的峰值来揭示。可以通过允许调查不同组织层级的参数来调整分辨率。在真实网络和人工网络上的测试都给出了很好的结果。
Many networks in nature, society and technology are characterized by a mesoscopic level of organization, with groups of nodes forming tightly connected units, called communities or modules, that are only weakly linked to each other. Uncovering this community structure is one of the most important problems in the field of complex networks. Networks often show a hierarchical organization, with communities embedded within other communities; moreover, nodes can be shared between different communities. Here, we present the first algorithm that finds both overlapping communities and the hierarchical structure. The method is based on the local optimization of a fitness function. Community structure is revealed by peaks in the fitness histogram. The resolution can be tuned by a parameter enabling different hierarchical levels of organization to be investigated. Tests on real and artificial networks give excellent results.