论文标题
多层网络中一致的贝叶斯社区恢复
Consistent Bayesian community recovery in multilayer networks
论文作者
论文摘要
揭示网络中节点之间的潜在关系是网络分析中最重要的任务之一。使用来自各种学科的工具和技术，为不同的情况开发了许多社区恢复方法。尽管最近对多层网络的社区恢复感兴趣，但对估计的准确性的理论结果很少，而且相差很远。给定一个多层，例如时间，网络和多层随机块模型，我们得出了边界，以在内部和块间连接参数之间进行足够的分离，以实现后部精确且几乎精确的社区恢复。这些条件与单层随机块模型的众所周知的社区检测阈值相当。一项仿真研究表明，派生的边界转化为分类精度，随着观察层层的增加，它会提高。
Revealing underlying relations between nodes in a network is one of the most important tasks in network analysis. Using tools and techniques from a variety of disciplines, many community recovery methods have been developed for different scenarios. Despite the recent interest on community recovery in multilayer networks, theoretical results on the accuracy of the estimates are few and far between. Given a multilayer, e.g. temporal, network and a multilayer stochastic block model, we derive bounds for sufficient separation between intra- and inter-block connectivity parameters to achieve posterior exact and almost exact community recovery. These conditions are comparable to a well known threshold for community detection by a single-layer stochastic block model. A simulation study shows that the derived bounds translate to classification accuracy that improves as the number of observed layers increases.
