Both of these learning methods utilize a richer set of features than the graph walk and weight tuning, which can consider only local information.
while the graph walk paradigm assigns lower importance to longer connecting paths (as described in Section 3), reranking and the path-constrained walker allow to discard short yet irrelevant paths, and by that eliminate noise at the top ranks of the retrieved list.