论文标题
混合分数+用于混合会员社区检测
Mixed-SCORE+ for mixed membership community detection
论文作者
论文摘要
混合分数是Jin等人提出的混合成员社区检测的最新方法。 （2017年），这是分数的延长（Jin，2015年）。在Note Jin等人的注释中。 （2018年），作者提出得分+作为分数的改进，以通过弱信号网络处理。在本文中，我们提出了一种基于混合分数和得分+设计的称为混合得分+的方法，因此混合得分+继承了混合分数和得分+的良好特性。在提出的方法中，当有K社区检测弱信号网络时，我们考虑K+1特征向量。我们还构建了狩猎和会员重建步骤，以解决混合成员社区发现的问题。与几种基准方法相比，数值结果表明，混合得分+分别在Polblogs网络和两个弱信号网络Simmons和CalTech上提供了显着改善，分别为54/1222、125/1137和94/590。此外，混合得分+在快照自我网络上表现出色。
Mixed-SCORE is a recent approach for mixed membership community detection proposed by Jin et al. (2017) which is an extension of SCORE (Jin, 2015). In the note Jin et al. (2018), the authors propose SCORE+ as an improvement of SCORE to handle with weak signal networks. In this paper, we propose a method called Mixed-SCORE+ designed based on the Mixed-SCORE and SCORE+, therefore Mixed-SCORE+ inherits nice properties of both Mixed-SCORE and SCORE+. In the proposed method, we consider K+1 eigenvectors when there are K communities to detect weak signal networks. And we also construct vertices hunting and membership reconstruction steps to solve the problem of mixed membership community detection. Compared with several benchmark methods, numerical results show that Mixed-SCORE+ provides a significant improvement on the Polblogs network and two weak signal networks Simmons and Caltech, with error rates 54/1222, 125/1137 and 94/590, respectively. Furthermore, Mixed-SCORE+ enjoys excellent performances on the SNAP ego-networks.
