【摘要】 社区发现是复杂网络研究领域的一个热点问题，目前已经有许多局部社区发现算法被提出用于快速发现高质量的社区，不过它们往往存在种子节点依赖或是稳定性问题。因此，部分算法试图根据核心节点被邻居高度包围且相互之间距离较远的拓扑特性来精确地锁定种子节点以避免上述问题，但距离的计算使得其时间复杂度较高。文中提出了一种基于核心节点影响力的社区发现方法 CDIC，该方法首先根据核心节点的拓扑特性和网络邻接信息寻找所有可能是核心的节点，之后利用真正核心节点影响力较高的性质和标签传播的思想来扩张社区，并淘汰被误选为核心的节点以避免种子依赖问题，同时不涉及最短距离的计算也保证了较低的时间复杂度，最后依据相似度理论提出了一种社区对节点的吸引力来合并特异节点，以保证算法结果的稳定性。将CDIC与6种经典算法以及2种近年来提出的算法在64个人工网络和4个真实网络上进行仿真实验，并对其社区划分结果对应的标准化互信息值和纯度进行了比较，结果表明了CDIC的有效性。更多还原

【Abstract】 Community discovery is a hot topic in the field of complex networks.Many local community detection algorithms have been proposed to quickly discover high-quality communities,but most of them have seed-dependent or stability problems.Some algorithms try to accurately find the seed nodes according to the topology characteristics of the core nodes that they are highly surrounded by neighbors and far away from each other to avoid the above problems.But the calculation of distance makes its time complexity is high.In this paper,a community detection method based on influence of core nodes(CDIC)is proposed.This method first searches for all possible core nodes according to the topological characteristics of core nodes and network adjacency information.Then it uses the higher influential of true core nodes and the idea of label propagation to expand the communities and eliminate nodes wrongly selected as the core to avoid the seed-dependent problems.Besides,the calculation without distance also ensures low time complexity.Finally,a community attraction to nodes based on the similarity theory is proposed to merge specific nodes to ensure the stability of the results.The normalized mutual information and purity of the proposed method,6classic algorithms and 2algorithms proposed in recent years are compared on 64artificial networks and 4real networks.The results show the effectiveness of CDIC.更多还原