node ranking in labeled directed graphs
Our work is motivated by the problem of ranking hyper-linked documents for a given query. Given an arbitrary directed graph with edge and node labels, we present a new flow-based model and an efficient method to dynamically rank the nodes of this graph with respect to any of the original labels. Ranking documents for a given query in a hyper-linked document set and ranking of authors/articles for a given topic in a citation database are some typical applications of our method. We outline the structural conditions that the graph must satisfy for our ranking to be different from the traditional  PageRank . We have built a system using two indices that is capable of dynamically ranking documents for any given query. We validate our system and method using experiments on a few datasets: a crawl of the IBM Intranet (12 million pages), a crawl of the  www  (30 million pages) and the DBLP citation dataset. We compare our method to existing schemes for topic-biased ranking that require a classifier and the traditional  PageRank . In these experiments, we demonstrate that our method is well suited for fine-grained ranking and that our method performs better than the existing schemes. We also demonstrate that our system can obtain an improved ranking with very little impact on query time.
flow-based model pagerank random surfer model search in context search intranet search link structure citation graph context-sensitive ranking web graph