论群落结构与群落特征的联合恢复

2018 56th Annual Allerton Conference on Communication, Control, and Computing (Allerton) Pub Date : 2018-10-01 DOI:10.1109/ALLERTON.2018.8636058

Jisang Yoon, Kangwook Lee, Changho Suh

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引用次数: 10

摘要

我们研究了从标记图观测中恢复K个群落及其特征的问题。我们假设观察图的边缘是根据对称随机块模型(SBM)生成的，并且每个节点的标签是相应社区特征的噪声和部分观察版本。我们描述了这个问题的信息理论极限，然后提出了一个计算效率高的算法来达到信息理论极限。

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On the Joint Recovery of Community Structure and Community Features

We study the problem of recovering both K communities and their features from a labeled graph observation. We assume that the edges of an observed graph are generated as per the symmetric Stochastic Block Model (SBM), and that the label of each node is a noisy and partially-observed version of the corresponding community feature. We characterize the information-theoretic limit of this problem, and then propose a computationally efficient algorithm that achieves the information-theoretic limit.

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来源期刊

2018 56th Annual Allerton Conference on Communication, Control, and Computing (Allerton)

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