This was an excellent video lecture on latent factor models by Peter Hoff. The speaker goes over various other models that may fit social network data. He presents a comparison of the models to a sample data set of a highschool. The comparisons show that the latent factor model is able to fit characteristics of real data, such as the total number of triangle relationships in the network or high correlation between connections of students with the same race, better than the other models.
In the second part of the lecture he gives a few examples and shows the performance of each of the three presented models on the examples. Next he shows mathematically why latent factor models are more flexible and much more general than the other two models. He references some software he’s written to aid in latent factor modeling of relational data on his website. He provides a few more examples and in particular data in relational arrays rather than just social network data. He goes into some depth describing how relational arrays can fit into latent factor models as well.
This was was an excellent introduction to latent factor models albeit a bit heavy with statistics for most people. It gives a really good coverage of the three other statistical models presented and how they compare to the latent factor model.