Stanford engineers have developed a patented algorithm that improves search results from ranking the objects of a database when viewed as a graph (e.g. a web graph). This system, Random Alpha Pagerank (RAPr), computes the importance of pages in a web graph using a random variable as a teleportation coefficient (compared to the standard PageRank algorithm which assumes a constant coefficient). This approach uncovers new characteristics that can be used to provide more relevant results for web searches, web spam detection, or gene/protein classification. It can also be used to derive more meaningful measures of importance by incorporating user behavior or domain specific knowledge. Stage of Research
The inventors have completed a study that shows spam ranking with RAPr has a meaningful improvement in the performance of web-spam detection. They have also demonstrated that the model is valid for measured user behavior on the web.