The topic-specific preference value P t (w) for each word w is its random jumping probability with the constraint that w∈V P t (w) = 1 given topic t. A large R t (•) indicates a word is a good candidate keyword in topic t. We denote this original version of the Topical PageRank as TPR.
we also tried the standard LDA model and the author-topic model on our data set and found that our proposed topic model was better or at least comparable in terms of finding meaningful topics.