社交网络分析与挖掘 第六课：网页排序

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The History of PageRank

• PageRank is a link analysis algorithm which assigns a numerical weighting to each Web page，with the purpose of "measuring"relative importance.
• Based on the hyperlinks map
• An excellent way to prioritize the results of web keyword searches
• PageRank was proposed by Larry Page（hencethe name Page-Rank）and Sergey Brin in 1998.
• Shortly after，Page and Brin founded Google.

PageRank:the intuitive idea

• Although page E hasmore"point to"links than page C, page C is more important than page E.
• One reason is that page C is linked by page B whose is the most important page in this network.

A Simple Version of PageRank

\[ R(u)=c \sum_{v \in B_{u}} \frac{R(v)}{N_{v}} \]

• u：a web page
• B(u)：the set of u‘s backlinks（point-to-links）
• N(v)：the number of forward links of page v
• c：the normalization factor to make
  \[ \|\mathrm{R}\|_{\mathrm{L} 1}=\left|\mathrm{R}_{1}+\ldots+\mathrm{R}_{\mathrm{n}}\right| \]

An example of Simplified PageRank

A Problem with Simplified PageRank

A loop:

During each iteration,the loop accumulates rank but never distributes rank to other pages!

Fix the problem:what PageRank does

• Randomly walking and also randomly jumping
  • the“random surfer”simply keeps clicking successive links at random，but periodically “gets bored”and jumps to a random page based on the distribution
    

The actual PageRank

\[ R^{\prime}(u)=c_{1} \sum_{v \in B_{u}} \frac{R^{\prime}(v)}{N_{v}}+\mathrm{c}_{2} E(u) \]

• E(u)：a distribution of ranks of web pages that "users"jump to when they"gets bored"after successive links at random.

An example of PageRank

Convergence Experiments

• PR（322 Million Links）：52 iterations
• PR（161Million Links）：45 iterations
• Scaling factor is roughly linear in logn
  

Conclusion

• PageRank rank web pages based on their locations in the web graph structure
• PageRank uses information which is external to the web pages-backlinks
• Backlinks from important pages are more significant than backlinks from common pages
• The structure of the web graph is very useful for information retrieval tasks.

以上内容出自小象学院

社交网络分析与挖掘 第六课：网页排序

标签：ges   map   strong   display   ext   hyper   link   mos   span   

原文地址：https://www.cnblogs.com/KisInfinite/p/11633540.html