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摘要翻译：
本文讨论了一种用于对称关系数据推断和预测的潜变量模型。该模型基于特征值分解的思想，将两个节点之间的关系表示为潜在特征向量的加权内积。这个“特征模型”推广了其他流行的潜在变量模型，如潜在类和距离模型：它从数学上表明，任何潜在类或距离模型都有一个特征模型的表示，但反之亦然。在三个实际数据集的背景下，本征模型具有与其他两个模型相同或更好的样本外预测性能，从而检验了这一问题的实际含义。
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英文标题：
《Modeling homophily and stochastic equivalence in symmetric relational
data》
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作者：
Peter D. Hoff
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最新提交年份：
2007
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分类信息：
一级分类：Statistics 统计学
二级分类：Methodology 方法论
分类描述：Design, Surveys, Model Selection, Multiple Testing, Multivariate Methods, Signal and Image Processing, Time Series, Smoothing, Spatial Statistics, Survival Analysis, Nonparametric and Semiparametric Methods
设计，调查，模型选择，多重检验，多元方法，信号和图像处理，时间序列，平滑，空间统计，生存分析，非参数和半参数方法
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英文摘要：
This article discusses a latent variable model for inference and prediction of symmetric relational data. The model, based on the idea of the eigenvalue decomposition, represents the relationship between two nodes as the weighted inner-product of node-specific vectors of latent characteristics. This ``eigenmodel'' generalizes other popular latent variable models, such as latent class and distance models: It is shown mathematically that any latent class or distance model has a representation as an eigenmodel, but not vice-versa. The practical implications of this are examined in the context of three real datasets, for which the eigenmodel has as good or better out-of-sample predictive performance than the other two models.
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PDF链接：
https://arxiv.org/pdf/711.1146
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