MODELS AND ALGORITHMS FOR PAGERANK SENSITIVITY

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MODELS AND ALGORITHMS FOR PAGERANK SENSITIVITY ( models-and-algorithms-for-pagerank-sensitivity )

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Table 4.6 – Spam classication performance. Our performance baseline includes all the features from [Becchetti et al., 2008]. Each row represents adding features from either RAPr or the derivative based on a particular Beta distribution or value of α. The results are averaged over 50 repetitions of 10-fold cross validation with a 10-bag J48 decision tree classifier.After adding features based on RAPr and the derivative, we observe an improvement in the f -score. Consequently, these features uncover new information in the graph that is not expressed by PageRank. Baseline Beta(1.5,0.5,0,0.99) Beta(-0.5,-0.5,0.3,0.99) Beta(0.5,1.5,0,0.99) Beta(10,10,0.3,0.7) Beta(1,1,0,1) Beta(2,16,0,1) Derivative (α = 0.75) Derivative (α = 0.85) Derivative (α = 0.95) 4.8 ⋅ applications 93 Precision Recall 0.694 0.558 0.692 0.557 0.698 0.564 0.695 0.561 0.690 0.560 0.698 0.562 0.699 0.562 0.697 0.563 0.697 0.561 0.700 0.560 f-score False Ratio 0.618 0.034 0.617 0.034 0.624 0.033 0.621 0.034 0.620 0.034 0.622 0.033 0.623 0.033 0.623 0.033 0.622 0.033 0.620 0.033 Positive False Nega- tive Ratio 0.442 0.443 0.436 0.439 0.442 0.438 0.438 0.437 0.439 0.440 and fails to improve on the baseline. Unlike many of the other metrics inves- tigated in the baseline performance, there is no tuning of the RAPr metrics for spam ranking. If we combined RAPr and TrustRank, for example, it may be possible to achieve even better performance.

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