Finding scientific gems with Google’s PageRank algorithm

Finding scientific gems with Google's PageRank algorithm

We apply the Google PageRank algorithm to assess the relative importance of all publications in the Physical Review family of journals from 1893 to 2003. While the Google number and the number of citations for each publication are positively correlated, outliers from this linear relation identify some exceptional papers or “gems” that are universally familiar to physicists. © 2006 Published by ...

Finding the Hidden Gems: Recommending Untagged Music

We have developed a novel hybrid representation for Music Information Retrieval. Our representation is built by incorporating audio content into the tag space in a tag-track matrix, and then learning hybrid concepts using latent semantic analysis. We apply this representation to the task of music recommendation, using similarity-based retrieval from a query music track. We also develop a new ap...

PageRank Algorithm and Development

Finding Relation between PageRank and Voter Model

Estimating influence of a node is an important problem in social network analyses. We address this problem in a particular class of model for opinion propagation in which a node adopts its opinion based on not only its direct neighbors but also the average opinion share over the whole network, which we call an extended Voter Model with uniform adoption (VM). We found a similarity of this model ...

PageRank algorithm and Monte Carlo methods in PageRank Computation

PageRank is the algorithm used by the Google search engine for ranking web pages. PageRank Algorithm calculates for each page a relative importance score which can be interpreted as the frequency of how often a page is visited by a surfer. The purpose of this work is to provide a mathematical analysis of the PageRank Algorithm. We analyze the random surfer model and the linear algebra behind it...