Thus we propose an LDA with block priors, hence BP-LDA, as shown in Figure 4, which has the following generative process: Unlike conventional LDA, which uses an uninformative topic prior, BP-LDA puts a Dirichlet prior π on each block to capture the block's topic distribution and use it as an informative prior when drawing each document's topic distribution.
a document's topic distributioni.e., what the document is about-is not just informed by the words present in the document but the broader context of its network neighborhood.