基于节点间依赖度的社团结构划分方法

Partitioning community structure in complex networks based on node dependent degree

本文提出了一种基于节点间依赖度的在复杂网络中划分社团结构的算法,定义了节点对其邻居的依赖度以及节点对社团的依赖度和条件依赖度.算法的基本要点是优先将最大依赖度不小于其他节点且有惟一依赖节点的节点划分到社团,并将对社团的依赖度或条件依赖度达到一定值的节点吸收进社团,直到所有节点都得到准确的社团划分.本算法在几个实际网络的测试上,都成功地划分出了满足条件的社团,并且对社团结构已知的网络的划分结果符合实际情况.

In this paper, we present a new approach to partitioning communities in a complex network via degree of dependence of nodes . We define the dependence degree of a node on its neighbors, the dependencetce degree and the conditional dependence degree of a node on a cluster. The main point of the approach is to partition the nodes, which have the biggest dependence degree and are only dependent on nodes, firstly to clusters, then to absorb nodes whose dependence degree or conditional dependence degree on cluster gets the right value, until all the nodes are partitioned to the right communities. The the network whose partition of our approach in some real-world network satisfies the definition of communities, and in communities are already known, our partition method fits the physical truth.