Abstract：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.
郭红，黄佳鑫，郭昆. 基于增广边簇序列的重叠层次社区发现*[J]. 模式识别与人工智能, 2015, 28(9): 828-838.
GUO Hong, HUANG Jia-Xin, GUO Kun. Discovery of Overlapping and Hierarchical Communities Based on Extended Link Cluster Sequence. , 2015, 28(9): 828-838.
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2015, Vol. 28 
