摘要： 针对现有社区检测算法复杂度高、运行速度慢等问题,以模块度最大化为优化目标,提出一种社区结构自适应检测算法。该算法具有幂律分布属性,能扩展至超大型网络,并且包含近似因子可保证被检测出的社区结构质量。在社区结构已知的合成网络和真实数据上的实验结果表明,该算法的检测性能优于FacetNet和Blondel等自适应检测算法。

关键词: 动态社交网络, 模块度, 社区检测, 幂律, 近似因子

Abstract: Existing community detection algorithms have higher complexity and lower speed.Aiming at this problem,an adaptive community structure detection algorithm in social network is proposed to maximize modularity.This algorithm has the advantages of power-law distribution property.It is scalable for very large network and possesses approximation factors to ensure the quality of its detected community structure.To certify the proposed algorithm,this paper conducts extensive experiments on both synthesized network with known community structures and real-world traces.Experimental results show that the detection performance of the proposed algorithm is better than other adaptive methods,like FacetNet and Blondel algorithms.

Key words: dynamic social network, modularity, community detection, power-law, approximation factor

中图分类号: 

• TP393