Variational Bayesian inference algorithms for infinite relational model of network data
Variational Bayesian inference algorithms for infinite relational model of network data
复制标题
网络数据无限关系模型的变分贝叶斯推理算法
DOI：
10.1109/tnnls.2014.2362012
复制
发表时间：
2015
影响因子：
10.4
通讯作者：
and Kazushi Ikeda
中科院分区：
文献类型：
--
作者：
Takuya Konishi;Takatomi Kubo;Kazuho Watanabe;and Kazushi Ikeda
网络数据表示的是一种对象之间的关系，如Web上的社交网络和超链接。已经提出了许多统计模型来分析这些数据。为了对网络的簇结构进行建模，提出了无限关系模型（Infinite Relational Model，简称RRM）作为随机块模型的贝叶斯非参数扩展。本文基于变分贝叶斯（VB）推理方法，推导了网络数据分类的推理算法。在给出标准VB推理后，我们导出了折叠VB（CVB）推理及其变体零阶CVB推理。我们使用六个真实的网络数据集的推理算法的性能进行了比较。在大多数数据集中，CVB推理优于VB推理，并且在密集网络中差异尤其大。
Network data show the relationship among one kind of objects, such as social networks and hyperlinks on the Web. Many statistical models have been proposed for analyzing these data. For modeling cluster structures of networks, the infinite relational model (IRM) was proposed as a Bayesian nonparametric extension of the stochastic block model. In this brief, we derive the inference algorithms for the IRM of network data based on the variational Bayesian (VB) inference methods. After showing the standard VB inference, we derive the collapsed VB (CVB) inference and its variant called the zeroth-order CVB inference. We compared the performances of the inference algorithms using six real network datasets. The CVB inference outperformed the VB inference in most of the datasets, and the differences were especially larger in dense networks.