Mutual Information in Community Detection with Covariate Information and Correlated Networks
Mutual Information in Community Detection with Covariate Information and Correlated Networks
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DOI：
10.1109/allerton.2019.8919733
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发表时间：
2019-09
期刊：
影响因子：
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通讯作者：
Vaishakhi Mayya;G. Reeves
中科院分区：
文献类型：
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作者：
Vaishakhi Mayya;G. Reeves
当存在有关节点标签的协变量信息并且观察多个相关网络时，我们研究社区检测问题。我们提供了每个节点互信息的渐近上限以及称为 MMSE 矩阵的多元性能度量的启发式分析。这些结果表明，看似非常不同类型的信息的组合效应可以根据涉及加性高斯噪声中的低维估计问题的公式来明确地表征。我们的分析得到了数值模拟的支持。
We study the problem of community detection when there is covariate information about the node labels and one observes multiple correlated networks. We provide an asymptotic upper bound on the per-node mutual information as well as a heuristic analysis of a multivariate performance measure called the MMSE matrix. These results show that the combined effects of seemingly very different types of information can be characterized explicitly in terms of formulas involving low-dimensional estimation problems in additive Gaussian noise. Our analysis is supported by numerical simulations.