基于局部增量超点Louvain剪枝技术的社区发现

发布时间：2019-01-17 08:33

【摘要】：为提高大型网络的社区发现精度和效果,解决叶节点存在的局部极值化问题,提出基于局部模块性增量超点Louvain剪枝技术的动态社区发现方法。首先,对网络社区进行模型定义,并给出社区发现的模块度函数,同时针对传统模块度函数存在的叶节点处置问题,对模块度函数进行改进;其次,在进行模块度函数改进基础上,针对叶节点问题利用超节点构建Louvain剪枝技术;最后,通过在社区发现算例上实验对比显示,所提算法相对于对比算法的模块度指标提升7.2%以上,验证了所提算法有效性。
[Abstract]:In order to improve the accuracy and effect of community discovery in large networks and to solve the problem of local extremum in leaf nodes, a dynamic community discovery method based on local modular incremental super-point Louvain pruning technique is proposed. Firstly, the model of the network community is defined, and the module degree function found by the community is given. At the same time, the module degree function is improved to deal with the leaf node problem existing in the traditional module degree function. Secondly, on the basis of improving the modular degree function, the Louvain pruning technology is constructed by using supernodes to solve the problem of leaf nodes. Finally, the experimental results show that the proposed algorithm is more than 7.2% higher than that of the contrast algorithm, which verifies the effectiveness of the proposed algorithm.
【作者单位】： 四川建筑职业技术学院信息工程系;
【分类号】：O157.5;TP301.6
，

本文编号：2409810