基于边标签传播的复杂网络社区识别方法

针对传统基于标签传播的复杂网络重叠社区识别算法难以准确识别重叠节点的缺陷,本文通过分析边与其邻居边的关系,提出用来评估边归属社区的归属密度函数及归属倾向性函数,并在此基础上设计一种基于边标签传播的重叠社区识别方法(OLLP).该方法首先以每条边连接2个节点中度高的节点标签作为该边的标签;然后通过分析边的归属密度与归属倾向性迭代更新边标签,最终标签相同的边属于同一社区.在基准网络与真实网络数据集上进行测试,并与多个具有代表性的算法进行比较,实验结果表明了OLLP算法的有效性和可行性.

Community Detection in Complex Networks Based on Link Label Propagation

Since traditional overlapping community detection methods in complex networks based on label propagation that could not detect overlapping nodes accurately, this paper presented link attribution density and link attribution orientation functions through analyzing the relationship between each link and its neighbor links to assess the attribution community of each link.On this basis, overlapping community detection method based on link label propagation (OLLP) was designed.Firstly, OLLP used the label of every link to the node label which possesses the higher degree when connected by the link, and then updated the label repeatedly through analyzing attribution density and attribution orientation of the link.Finally, identical label links were attributed to the same community.By testing on both synthetic and real-world networks, and comparing with multiple representative algorithms, the experimental results verify the validity and feasibility of OLLP.