An overlapping community detection algorithm based on density peaks
An overlapping community detection algorithm based on density peaks
复制标题
一种基于密度峰值的重叠社区检测算法
DOI：
10.1016/j.neucom.2016.11.019
复制
发表时间：
2017-02-22
期刊：
影响因子：
6
通讯作者：
Shi, Xiaohu
中科院分区：
文献类型：
--
作者：
Bai, Xueying;Yang, Peilin;Shi, Xiaohu
许多现实世界的网络包含重叠的社区，如蛋白质-蛋白质网络和社交网络。重叠社区检测在研究这些网络的隐含结构中起着重要作用。提出了一种新的基于密度峰值的重叠社区检测算法(OCDDP)。OCDDP使用基于相似度的方法设置节点间的距离，通过三步过程选择社区的核心和成员向量来表示节点的属性。在合成网络和社会网络上的实验证明，OCDDP是一种有效且稳定的重叠社区检测算法。与现有的顶级方法相比，它在那些“简单”结构的网络上比在那些不太“复杂”的网络上表现得更好。
Many real-world networks contain overlapping communities like protein-protein networks and social networks. Overlapping community detection plays an important role in studying hidden structure of those networks. In this paper, we propose a novel overlapping community detection algorithm based on density peaks (OCDDP). OCDDP utilizes a similarity based method to set distances among nodes, a three-step process to select cores of communities and membership vectors to represent belongings of nodes. Experiments on synthetic networks and social networks prove that OCDDP is an effective and stable overlapping community detection algorithm. Compared with the top existing methods, it tends to perform better on those "simple" structure networks rather than those infrequently "complicated" ones.