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PageRank (PR) is an algorithm used by Google Search to rank web pages in their search engine results. It is named after both the term "web page" and co-founder Larry Page. PageRank is a way of measuring the importance of website pages. According to Google:
The analysis highlights History and Applications as prominent areas in the source structure around PageRank.
Source areas are shown by the number of related topics found in each part of the analysis. Use smaller areas too: they can reveal specialized angles and content gaps.
Smaller areas are not necessarily less important. They contain fewer connections in this analysis and can be useful for finding specialized angles or coverage gaps.
High-confidence facts extracted from structured source data. Use them as anchors for further research.
Browse the complete topic structure, not only the most central items. Less prominent entities and concepts can reveal missing angles, specialized context and useful research gaps. Each item opens a new analysis centered on that subject.
Deeper signals for content research, entity SEO and topical coverage. The plain-language headings explain what each technical view is useful for.
The extracted context around PageRank shows recurring relationship patterns in the source. For example, PageRank → Analytic Hierarchy Process, Baidu, Bradley Love, China, Edmund Landau, Francis Narin, Gabriel Pinski, Google, He, IDD Information Services, In, Larry Page, Li, Li's, PageRank's, RankDex, Robin Li, Steven Sloman, The, Thomas Saaty Another extracted example is PageRank → API, Google, Google Toolbar, Google Webmaster Tools, However, In March, In October, It, Many, Matt Cutts, November, October, On April, PageRank Data, The, The Google Toolbar, Toolbar PageRank, We've, Webmaster Tools. Use these groups to spot repeated connection types before inspecting the individual relationships.
Use these terms to understand the vocabulary surrounding the topic, not as a checklist for keyword stuffing.
page google links pages algorithm used link number search web value displaystyle probability one results rank ranking distribution engine many
TTTA extracted 171 structured relationships around PageRank. Examples in this analysis include PageRank → is a → way of measuring the importance of website pages and cnn.com or mayoclinic.org → instance of → taking into consideration authority hubs. The table shows each extracted connection, where it came from and its confidence.
| Subject | Predicate | Object | Confidence | Src |
|---|---|---|---|---|
| PageRank | is a | way of measuring the importance of website pages | 0.90 | text |
| cnn.com or mayoclinic.org | instance of | taking into consideration authority hubs | 0.80 | text |
| number of links from the home page.A Web crawler may use PageRank as one of a number of importance metrics it uses to determine which URL to visit during a crawl of the web | instance of | by looking at each website's signals of importance and prioritizing content based on factors | 0.80 | text |
| the number of inbound | instance of | though there are others listed | 0.80 | text |
| outbound links for a URL | instance of | though there are others listed | 0.80 | text |
| and the distance from the root directory on a site to the URL.The PageRank may also be used as a methodology to measure the apparent impact of a community like the Blogosphere on the overall Web itself | instance of | though there are others listed | 0.80 | text |
| PageRank | has application | In | 0.60 | section |
| PageRank | has application | Pakistan | 0.60 | section |
| PageRank | has application | Structural Deep Democracy | 0.60 | section |
| PageRank | has application | SD2 | 0.60 | section |
| PageRank | has application | Contact Youth | 0.60 | section |
| PageRank | has application | More | 0.60 | section |
The concept neighborhoods around PageRank bring nearby vocabulary together. In this analysis, examples include Google, Links and Page. Use the clusters to find adjacent concepts and terminology that may deserve separate research.
For PageRank, one of the stronger structural bridges in this analysis connects PageRank with History. Bridges highlight paths between different parts of the map and can reveal research angles that are easy to miss in a flat list.
TTTA analyzes the structure around PageRank to surface related topics, entities, relationships, concept neighborhoods and bridge connections. Use the map to explore areas such as History & Applications, including less central topics that may reveal useful research gaps. Automatically extracted connections are research leads rather than rewritten encyclopedia content.
Source: Wikipedia — PageRank · EN edition · Analysis: TopicsToTalkAbout