一种基于边界节点识别的复杂网络局部社区发现算法

Detecting Local Community Structure Based on the Identification of Boundary Nodes in Complex Networks

在网络日益巨大化和复杂化的背景下,挖掘全局网络的社区结构代价较高。因此,基于给定节点的局部社区发现对研究复杂网络社区结构有重要的应用意义。现有算法往往存在着稳定性和准确性不高,预设定阈值难以获取等问题。该文提出一种基于边界节点识别的复杂网络局部社区发现算法,全面比较待合并节点的连接相似性进行节点聚类;并通过边界节点识别控制局部社区的规模和范围,从而获取给定节点所属社区的完整信息。在计算机生成网络和真实网络上的实验和分析证明,该算法能够自主挖掘给定节点所属的局部社区结构,有效地提升局部社区发现稳定性和准确率。

In the context that social network becomes more and more complicated and huge, it is extremely difficult and complex to mine the global community structures of large networks. Therefore, the local community detection has important application significance for studying and understanding the community structure of complex networks. The existing algorithms often have some defects, such as low accuracy and stability, the preset thresholds difficult to obtain, etc.. In this paper, a local community detecting algorithm is proposed based on boundary nodes identification, and a comprehensive consideration of the external and internal link similarity of neighborhood nodes for community clustering is given. Meanwhile, the method can effectively control the scale and scope of the local community based on the boundary node identification, so as the complete structure information of the local community is obtained. Through the experiments on both computer-generated and real-world networks, the results show that the proposed algorithm can automatically mine local community structure from the given node without predefined parameters, and improve the performance of local community detection in stability and accuracy.