基于节点相似性的加权复杂网络BGLL社团检测方法

BGLL Community Detection for Weighted Complex Network Based on Node Similarity

针对加权复杂网络中的重叠社团检测问题,提出了一种面向加权网络的基于Jaccard系数的BGLL模块密度优化算法(Modularity Density and Jaccard Based BGLL, DBGLLJ).利用节点重要度重构网络,根据模块度增益作为阶段函数和模块密度增益作为目标函数进行网络硬划分,并提出了结合改进的Jaccard系数的重叠检测方法.为验证算法,选择了3种算法在LFR网络和真实网络中进行测试,结果表明:在标准LFR网络和真实网络中,DBGLLJ算法检测效果较优,具有较高的重叠模块度以及重叠检测准确性,且运算效率较好.将所提算法应用于现实复杂机电系统因效性网络,重叠检测结果较好,具有较高的参考价值.

Aiming at the problem of weighted overlapping community detection in complex network, the DBGLLJ(modularity Density and Jaccard based BGLL) method for weighted network is proposed. The network is firstly reconstructed by the importance of node, and then the network is divided into a series of segment according to the modularity gain and the module density gain as the phase function. The overlapping detection method combined with the improved Jaccard index is also proposed. In order to verify the proposed method, three algorithms were selected for testing in LFR networks and real-life networks. The results show that DBGLLJ method is better than the others in standard LFR networks and real-life networks, and has higher overlapping modularity which shows the effectiveness and accuracy of the proposed method. The proposed method is also applied to the reality network of the complex electromechanical system. The overlapping detection result is better and has higher reference value.