Updating PageRank with Iterative Aggregation

We present an algorithm for updating the PageRank vector [1]. Due to the scale of the web, Google only updates its famous PageRank vector on a monthly basis. However, the Web changes much more frequently. Drastically speeding the PageRank computation can lead to fresher, more accurate rankings of the webpages retrieved by search engines. It can also make the goal of real-time personalized rankings within reach. On two small subsets of the web, our algorithm updates PageRank using just 25% and 14%, respectively, of the time required by the original PageRank algorithm. Our algorithm uses iterative aggregation techniques [7, 8] to focus on the slow-converging states of the Markov chain. The most exciting feature of this algorithm is that it can be joined with other PageRank acceleration methods, such as the dangling node lumpability algorithm [6], quadratic extrapolation [4], and adaptive PageRank [3], to realize even greater speedups (potentially a factor of 60 or more speedup when all algorithms are combined)

Google's pagerank and beyond: the science of search engine rankings

Why doesn't your home page appear on the first page of search results, even when you query your own name? How do other Web pages always appear at the top? What creates these powerful rankings? And how? The first book ever about the science of Web page rankings, Google's PageRank and Beyond supplies the answers to these and other questions and more. The book serves two very different audiences: the curious science reader and the technical computational reader. The chapters build in mathematical sophistication, so that the first five are accessible to the general academic reader. While other ch

A Kronecker product approximate preconditioner for SANs

this paper, we extend the nearest Kronecker product technique to approximate the Q matrix for a SAN with a Kronecker product, A1 A2 AN . Then, we take M = A 1 2 N as our SAN NKP preconditioner. Copyright c 2003 John Wiley & Sons, Ltd. 1

Information Retrieval and Web Search

Information retrieval is the process of searching within a document collection for information most relevant to a user’s query. However, the type of document collection significantly affects the methods and algorithms used to process queries. In this chapter we distinguish between two types of document collections: traditional and Web collections. Traditional information retrieval is search within small, controlled, nonlinked collections (e.g., a collection of medical or legal documents), whereas Web information retrieval is search within the world’s largest and linked document collection. In spite of the proliferation of the Web, more traditional nonlinked collections still exist, and there is still a place for the older methods of information retrieval