Multiplicative latent factor models for description and prediction of social networks
Multiplicative latent factor models for description and prediction of social networks
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DOI：
10.1007/s10588-008-9040-4
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发表时间：
2009-12-01
影响因子：
1.8
通讯作者：
Hoff, Peter D.
中科院分区：
文献类型：
--
作者：
Hoff, Peter D.
我们讨论了基于矩阵表示和对称性考虑的社交网络数据的统计模型。该模型可以包括回归项形式的已知预测者信息，并且可以通过发送者特定和接收者特定的潜在因素来表示额外的结构。该方法允许通过节点的潜在因素对社交网络进行图形化描述，并为预测网络数据中的缺失链接提供了框架。
We discuss a statistical model of social network data derived from matrix representations and symmetry considerations. The model can include known predictor information in the form of a regression term, and can represent additional structure via sender-specific and receiver-specific latent factors. This approach allows for the graphical description of a social network via the latent factors of the nodes, and provides a framework for the prediction of missing links in network data.