论文标题
通过稀疏光谱分解在网络中重叠的社区检测重叠
Overlapping community detection in networks via sparse spectral decomposition
论文作者
论文摘要
我们考虑了估计网络中社区成员资格重叠的问题，每个节点都可以属于多个社区。每个节点的几个社区都难以估计和解释，因此我们专注于稀疏节点成员资格向量。我们的算法基于稀疏的主要子空间估计，并具有迭代阈值。该方法在计算上是有效的，其计算成本等同于估计邻接矩阵的领先特征向量，并且与光谱聚类方法不同，不需要额外的聚类步骤。我们表明该算法的固定点对应于随机块模型版本下的校正节点成员身份。这些方法在模拟和现实世界网络上进行经验评估，显示出良好的统计性能和计算效率。
We consider the problem of estimating overlapping community memberships in a network, where each node can belong to multiple communities. More than a few communities per node are difficult to both estimate and interpret, so we focus on sparse node membership vectors. Our algorithm is based on sparse principal subspace estimation with iterative thresholding. The method is computationally efficient, with a computational cost equivalent to estimating the leading eigenvectors of the adjacency matrix, and does not require an additional clustering step, unlike spectral clustering methods. We show that a fixed point of the algorithm corresponds to correct node memberships under a version of the stochastic block model. The methods are evaluated empirically on simulated and real-world networks, showing good statistical performance and computational efficiency.
