基于信息传递和峰值聚类的自适应社区发现算法

信息传递是网络具有的基本特征，基于此提出了一种基于信息传递和峰值聚类的自适应社区发现算法。首先，定义了节点与邻居之间的信任度函数，每个节点基于信任度独立的向网络中扩散信息量。扩散结束后，节点总信息量即为峰值聚类中的密度；网络中节点之间的距离通过所含节点信息量的倒数替代。然后，提出一种自动选取核心节点方法并为核心节点分配不同社区，把剩余节点分配到与它距离最短的核心节点所在社区，完成社区划分。本算法的优点在于无需额外参数并且能够发现社区内部结构。实验结果表明本算法发现的社区结构更加接近网络真实社区结构。

An adaptive community detection method based on information transfer and density peaks

Information transfer is the basic feature of the network. Accordingly, an adaptive community detection method based on information transfer and density peaks is proposed. Firstly, the trust degree function between nodes and neighbors is defined, and each node independently spreads amount of information to the network based on the trust degree. After the diffusion, the total information amount of the node is the density of the density peaks. The distance between the nodes in the network is replaced by the reciprocal of the information amount of the destination node. Then, a method is proposed that can automatically select core nodes which are divided into different communities, and the remaining nodes are allocated to the community of the closest core node. The algorithm has the advantage that no additional parameters are needed and the internal structure of the community can be found. The experimental results show that.