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94 4 ⋅ random alpha pagerank summary By incorporating information from multiple random surfers simultane- ously, the RAPr model increases the flexibility of PageRank models consider- ably. We present theoretical results showing that it generalizes the properties of the PageRank vector. Whereas PageRank contains an oversight when ap- plied to real-world surfer data, we show that web browsing logs contain the information to compute the multi-surfer distribution for RAPr. These logs show that users follow links with probability 0.375. Next, we derive three algorithms to compute the expectation and standard deviation for the RAPr setup. Two of these algorithms just use PageRank solutions at multiple values of α. We present both theoretical and empirical error analysis for each algorithm. Thus, computing these quantities is not a problem. Finally, our analysis of the expectation and standard deviation shows that the expectation is closely aligned to PageRank, but the standard deviation is not. This holds both for web search networks and gene identification networks. The RAPr statistics also improve a spam classification task.PDF Image | MODELS AND ALGORITHMS FOR PAGERANK SENSITIVITY
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