Bayesian estimation of the latent dimension and communities in stochastic blockmodels
Bayesian estimation of the latent dimension and communities in stochastic blockmodels
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DOI：
10.1007/s11222-020-09946-6
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发表时间：
2019-04
影响因子：
2.2
通讯作者：
Francesco Sanna Passino;N. Heard
中科院分区：
文献类型：
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作者：
Francesco Sanna Passino;N. Heard
无向图的邻接或拉普拉斯矩阵的谱嵌入是在较低维潜在空间中表示网络的常用技术，具有最佳的理论保证。嵌入可用于估计网络的社区结构，在随机块模型框架中具有强一致性结果。从谱嵌入进行社区检测的标准算法的主要实际限制之一是必须提前指定社区的数量和嵌入的潜在维度。在本文中，提出了一种新颖的贝叶斯模型，用于同时自动选择潜在空间的适当维度和块的数量。讨论了有向图和二分图的扩展。该模型在模拟和现实世界的网络数据上进行了测试，显示出恢复潜在社区结构的良好性能。
Spectral embedding of adjacency or Laplacian matrices of undirected graphs is a common technique for representing a network in a lower dimensional latent space, with optimal theoretical guarantees. The embedding can be used to estimate the community structure of the network, with strong consistency results in the stochastic blockmodel framework. One of the main practical limitations of standard algorithms for community detection from spectral embeddings is that the number of communities and the latent dimension of the embedding must be specified in advance. In this article, a novel Bayesian model for simultaneous and automatic selection of the appropriate dimension of the latent space and the number of blocks is proposed. Extensions to directed and bipartite graphs are discussed. The model is tested on simulated and real world network data, showing promising performance for recovering latent community structure.