Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Modeling homophily and stochastic equivalence in symmetric relational
  data   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.

12
The categories of this paper are: nucl-th
30
The categories of this paper are: q-alg
4
The categories of this paper are: cond-mat
26
The categories of this paper are: bayes-an
34
The categories of this paper are: chem-ph
3
The categories of this paper are: physics
14
The categories of this paper are: math-ph
5
The categories of this paper are: gr-qc
31
The categories of this paper are: ao-sci
8
The categories of this paper are: hep-ex
24
The categories of this paper are: dg-ga
17
The categories of this paper are: q-fin
20
The categories of this paper are: acc-phys
2
The categories of this paper are: math
19
The categories of this paper are: eess
7
The categories of this paper are: hep-th