社交网络中基于模块度最大化的标签传播算法的研究

Research on label propagation algorithm based on modularity maximization in the social network

提出了一种利用模块度最大化与社区结构属性相结合的社区发现方法。首先,针对基于模块度最大化的标签传播算法中存在的时间复杂度高的问题,引入传播距离参数,依据"先传播,后合并"的原则,降低了社区合并导致整个网络需要更新带来的较高时间复杂度;其次,结合社区结构的概念提出了基于模块度最大化的标签传播算法(CDMM-LPA);最后,基于网络数据集,验证并分析了CDMM-LPA算法的可行性。实验结果表明,CDMM-LPA算法在降低了时间复杂度的同时,获得了较高的模块度值和更加稳定的强社区结构。

A kind of community detection method based on the combination of modularity and community structure attributes was proposed.Firstly,updating the whole network after communities merging every time could result in the high time complexity,therefore,introducing propagation distance parameter and ＂merger going after label propagation＂was utilized to reduce time complexity.Secondly,CDMM-LPA algorithm was proposed by combing label propagation with community structure.Finally,empirical analysis on data networks verified the validity of the approaches.The experimental results show that the CDMM-LPA algorithm has a high modularity value and a more stable community structure while reducing the time complexity.