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1.4 ⋅ other uses for pagerank 7 arbitrary graph, and that there are many ways to change and interpret the PageRank model. There are still other uses for PageRank. clustering Theproblemofclusteringistofindwaystodivideagraphinto pieces by separating the nodes into cohesive groups. One approach is to find a set of strongly related nodes, call that a group, remove it from the graph, and repeat until the graph is empty. PageRank helps find a group of strongly related nodes, as Andersen et al. [2006] demonstrate. They show that a modified personalized PageRank, where the surfer only resets to a single page ( called the target), produces a group of pages near the target. Further, they show that they can use a customized PageRank algorithm to compute these groups of nodes extremely quickly. sports ranking PageRankalsohelpstoranksportsteams.Intworecent contributions, both Langville [2009] and Govan et al. [2008] extend ideas originating from Keener [1993] by using PageRank to compute a ranking of sports teams. Instead of a random surfer, they posit a “fair-weather fan” who picks favorites between teams based on some set of statistics. A simple case is to use the win and loss records between teams to create a graph where two teams are connected from the loser to the winner. The PageRank vector of that graph gives a useful ordering of the teams. bioinformatics The GeneRank method by Morrison et al. [2005] pro- duces lists of genes that may be relevant to a microarray experiment. Because some of the entries in the microarray data are noisy, the exper- iment may not reveal all of the interesting genes activated in different conditions. GeneRank uses a surfer over known relationships between genes, where the surfer “restarts” with probability proportional to the activation level of the genes in the experiment. Its output is a set of genes “near” the genes with high activation levels. The goal of the method is to aid researchers working with microarray datasets to see which other genes are nearby using known relationships. Freschi [2007] uses a similar idea, which they call ProteinRank, to predict protein functions. The nodes of the relevant graph are proteins, and two proteins are connected if they physically interact, which yields a protein-protein interaction (PPI) network. Instead of gene annota- tions from a microarray experiment driving the random surfer behav- ior in the reset step, the ProteinRank algorithm uses known functional behaviors to direct the surfer to the relevant portion of the graph.PDF Image | Instagram Cheat Sheet
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