Topical link analysis for web search

Traditional web link-based ranking schemes use a single score to measure a page's authority without concern of the community from which that authority is derived. As a result, a resource that is highly popular for one topic may dominate the results of another topic in which it is less authoritative. To address this problem, we suggest calculating a score vector for each page to distinguish the contribution from different topics, using a random walk model that probabilistically combines page topic distribution and link structure. We show how to incorporate the topical model within both PageRank and HITS without affecting the overall property and still render insight into topic-level transition. Experiments on multiple datasets indicate that our technique outperforms other ranking approaches that incorporate textual analysis.