· 2014
Multilinear tensor regression for longitudinal relational data
- 178citations
- 2014year
Short summary
A novel multilinear tensor regression model estimates dependencies between relations in longitudinal network data, capturing patterns like reciprocity and transitivity.
AI-generated from the title and abstract; the full text is not read.
Key points
- Develops a multilinear tensor regression model for longitudinal relational data.
- Estimates dependencies between relations across different node pairs over time.
- Uses a separable (Kronecker-structured) regression parameter and covariance model.
- Can represent network patterns such as reciprocity and transitivity.
AI-generated from the title and abstract; the full text is not read.
Abstract
A fundamental aspect of relational data, such as from a social network, is the possibility of depen-dence among the relations. In particular, the relations between members of one pair of nodes may have an effect on the relations between members of another pair. This article develops a type of regression model to estimate such effects in the context of longitudinal and multivariate relational data, or other data that can be represented in the form of a tensor. The model is based on a gen-eral multilinear tensor regression model, a special case of which is a tensor autoregression model in which the tensor of relations at one time point are parsimoniously regressed on relations from previous time points. This is done via a separable, or Kronecker-structured, regression parameter along with a separable covariance model. In the context of an analysis of longitudinal multivariate relational data, it is shown how the multilinear tensor regression model can represent patterns that often appear in relational and network data, such as reciprocity and transitivity.
The authors' abstract, as published at the source., 2014 · DOI ↗
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Field: Computational Mathematics
Computational MathematicsMathematics