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第670行： − 正如上面解释的那样，试图通过随机跳转，为互联网上的每个网站分配网页排名。通过随机跳转可以找到一些在正常的搜索方法（如广度优先搜索和深度优先搜索）中找不到的边缘网站。+ − 在上述公式中，该算法的主要提升是添加了随机跳转，没有这些随机跳转，一些网页的排名可能就是0，这样是非常不好的。+ + − 第一个字母<math>\alpha</math>，代表的是随机跳转发生的概率。与此相对的是阻尼因子，对应的是<math>1 - \alpha</math>。+ + +
→随机跳转
As explained above, PageRank enlists random jumps in attempts to assign PageRank to every website on the internet. These random jumps find websites that might not be found during the normal search methodologies such as [[Breadth-First Search]] and [[Depth-First Search]].
As explained above, PageRank enlists random jumps in attempts to assign PageRank to every website on the internet. These random jumps find websites that might not be found during the normal search methodologies such as [[Breadth-First Search]] and [[Depth-First Search]].
正如上面解释的那样，PageRank通过随机跳转，尝试将PageRank分配给互联网上的每个网站。通过随机跳转可以找到一些在正常的搜索方法（如广度优先搜索和深度优先搜索）中找不到的边缘网站。
In an improvement over the aforementioned formula for determining PageRank includes adding these random jump components. Without the random jumps, some pages would receive a PageRank of 0 which would not be good.
In an improvement over the aforementioned formula for determining PageRank includes adding these random jump components. Without the random jumps, some pages would receive a PageRank of 0 which would not be good.
在上述公式中，该算法的主要提升是添加了随机跳转，没有这些随机跳转，一些网页的PageRank可能就是0，这样是非常不好的。
The first is <math>\alpha</math>, or the probability that a random jump will occur. Contrasting is the "damping factor", or <math>1 - \alpha</math>.
The first is <math>\alpha</math>, or the probability that a random jump will occur. Contrasting is the "damping factor", or <math>1 - \alpha</math>.
第一个为 <math>\alpha</math>，代表的是随机跳转发生的概率。与此相对的是阻尼因子，即 <math>1 - \alpha</math>。
: <math>R{(p)} = {\alpha\over N} + (1 - \alpha) \sum_{j\rightarrow i} {1\over N_j} x_j^{(k)}</math>
: <math>R{(p)} = {\alpha\over N} + (1 - \alpha) \sum_{j\rightarrow i} {1\over N_j} x_j^{(k)}</math>
Another way of looking at it:
Another way of looking at it:
从另一个角度来看
从另一个角度来看
: <math>R(A) = \sum {R_B\over B_\text{(outlinks)}} + \cdots + {R_n \over n_\text{(outlinks)}}</math>
: <math>R(A) = \sum {R_B\over B_\text{(outlinks)}} + \cdots + {R_n \over n_\text{(outlinks)}}</math>