基于增广边簇序列的重叠层次社区发现

Discovery of Overlapping and Hierarchical Communities Based on Extended Link Cluster Sequence

高质量重叠层次社区的挖掘和发现已成为社会网络研究热点,为更有效地发现社会网络中具有重叠层次性的社区结构,提出基于增广边簇序列的边社区发现算法(DLC_ECS).在产生包含所有可能密度参数对应的社区结构的增广边簇序列的基础上,找出全局最优的密度参数,发现全局最优的边社区结构,将识别的边社区结构转化为节点社区结构,发现具有重叠结构的社区.在该序列的基础上,提出层次边社区提取算法(HLCE_ECS),快速发现序列中的层次边社区结构,将识别的边社区结构转化为节点社区结构,发现同时具有重叠和层次结构的社区.在真实数据集和人工数据集上的实验表明,DLC_ECS具有更高的社区发现质量,HLCE_ECS能发现有意义的层次边社区结构.

The mining and discovery of overlapping and hierarchical communities is a hot topic in the area of social network research. Firstly, an algorithm, discovery of link conmunities based on extended link cluster sequence （ DLC ECS） , is proposed to detect overlapping and hierarchical communities in social networks efficiently. Based on the extended link cluster sequence corresponding to community structures with various densities, the optimal link community is detected after searching for the global optimal density. The link communities are transformed into the node communities, and thus the overlapping communities can be found out. Then, hierarchical link communities extraction based on extended link cluster sequence （ HLCE ECS ） is designed. Hierarchical link communities from the extended link cluster sequence is found by the proposed algorithm. The link communities are transformed into the node communities to find out the overlapping and hierarchical communities. Experimental results on are artificial and real-world datasets demonstrate that DLC ECS algorithm significantly improves the community quality and HLCE ECS algorithm effectively discovers meaningful hierarchical communities.