The effect of adjusting damping factor {\alpha} and tolerance {\tau} on iterations needed for PageRank computation is studied here. Relative performance of PageRank computation with L1, L2, and L{\infty} norms used as convergence check, are also compared with six possible mean ratios. It is observed that increasing the damping factor {\alpha} linearly increases the iterations needed almost exponentially. On the other hand, decreasing the tolerance {\tau} exponentially decreases the iterations needed almost exponentially. On average, PageRank with L{\infty} norm as convergence check is the fastest, quickly followed by L2 norm, and then L1 norm. For large graphs, above certain tolerance {\tau} values, convergence can occur in a single iteration. On the contrary, below certain tolerance {\tau} values, sensitivity issues can begin to appear, causing computation to halt at maximum iteration limit without convergence. The six mean ratios for relative performance comparison are based on arithmetic, geometric, and harmonic mean, as well as the order of ratio calculation. Among them GM-RATIO, geometric mean followed by ratio calculation, is found to be most stable, followed by AM-RATIO.
翻译：此处研究的是调整“ PageRank” 计算公式所需的阻力因子 ~alpha} 和容容度 ~Tau} 对 PageRank 计算公式的影响。 PageRank 计算法与 L1, L2, 和 L_infty} 标准作为趋同检查的相对性能也与六种可能的平均比率相比较。 观察到, 增加阻力因子 ~alpha} 线性能会增加所需的迭代几乎成指数。 另一方面, 降低容度 ~Tau} 和容度会急剧减少所需的迭代值。 平均而言, PageRank 与 L~ Iinfty} 的趋同标准是最快的, 然后是L2 规范, 然后是L1 规范。 对于大型图表, 超过某些容度 ~ tau} 的值, 可能会在一次迭代数中出现趋同。 相反,, 敏感度问题会开始出现, 导致计算结果在最大纬度限度内停止。 。 。 相对性性能比较的六比比比率以算法、 、 和平均比率计算法比重。
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