论文标题
通过正规张量分解在混合物多层网络上的社区检测
Community Detection on Mixture Multi-layer Networks via Regularized Tensor Decomposition
论文作者
论文摘要
我们研究了多层网络中社区检测的问题，其中一对节点可以以多种方式相关。我们引入了一个通用框架，即混合物多层随机块模型（MMSBM），其中包括许多早期模型作为特殊情况。我们提出了一种基于张量的算法（Twist），以揭示节点的全球/本地成员资格和层的成员资格。我们表明，随着节点的数量和/或层数的增加，扭曲过程可以准确地检测出较小的错误分类误差的社区。数值研究证实了我们的理论发现。据我们所知，这是对混合物多层网络进行张量分解的首次系统研究。该方法应用于两个真实数据集：全球交易网络和疟疾寄生虫基因网络，产生了新的有趣发现。
We study the problem of community detection in multi-layer networks, where pairs of nodes can be related in multiple modalities. We introduce a general framework, i.e., mixture multi-layer stochastic block model (MMSBM), which includes many earlier models as special cases. We propose a tensor-based algorithm (TWIST) to reveal both global/local memberships of nodes, and memberships of layers. We show that the TWIST procedure can accurately detect the communities with small misclassification error as the number of nodes and/or the number of layers increases. Numerical studies confirm our theoretical findings. To our best knowledge, this is the first systematic study on the mixture multi-layer networks using tensor decomposition. The method is applied to two real datasets: worldwide trading networks and malaria parasite genes networks, yielding new and interesting findings.
