The mathematics of PageRank are entirely general and apply to any graph or network in any domain. Thus, PageRank is now regularly used in bibliometrics, social and information network analysis, and for link prediction and recommendation. It's even used for systems analysis of road networks, as well as biology, chemistry, neuroscience, and physics.
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The PageRank algorithm outputs a probability distribution used to represent the likelihood that a person randomly clicking on links will arrive at any particular page. PageRank can be calculated for collections of documents of any size. It is assumed in several research papers that the distribution is evenly divided among all documents in the collection at the beginning of the computational process. The PageRank computations require several passes, called “iterations”, through the collection to adjust approximate PageRank values to more closely reflect the theoretical true value. Cartoon illustrating the basic principle of PageRank. The size of each face is proportional to the total size of the other faces which are pointing to it.[/caption]
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Another excellent guide is Google’s “Search Engine Optimization Starter Guide.” This is a free PDF download that covers basic tips that Google provides to its own employees on how to get listed. You’ll find it here. Also well worth checking out is Moz’s “Beginner’s Guide To SEO,” which you’ll find here, and the SEO Success Pyramid from Small Business Search Marketing.