Google's founders, in their original paper,[18] reported that the PageRank algorithm for a network consisting of 322 million links (in-edges and out-edges) converges to within a tolerable limit in 52 iterations. The convergence in a network of half the above size took approximately 45 iterations. Through this data, they concluded the algorithm can be scaled very well and that the scaling factor for extremely large networks would be roughly linear in {\displaystyle \log n} , where n is the size of the network.
However, the biggest contributing factors to a backlink’s effect on your rank is the website it’s coming from, measured by the acronym ART: authority, a measure of a site’s prestige/reliability — .edu and .gov sites are particularly high-authority); relevance, a measure of how related the site hosting the link is to the content; and trust, which is not an official Google metric, but relates to how much a site plays by the rules of search (i.e. not selling links) and provides good content.
Google recommends that all websites use https:// when possible. The hostname is where your website is hosted, commonly using the same domain name that you'd use for email. Google differentiates between the "www" and "non-www" version (for example, "www.example.com" or just "example.com"). When adding your website to Search Console, we recommend adding both http:// and https:// versions, as well as the "www" and "non-www" versions.
What i have learnt with comments only allow them if they give value to your blog i have used this for one of my main blogs bpd and me and it worked i have let comments threw witch were spamee and it just got a google page rank of 2 after a year learning by mistakes google page rank is always going to be a mystery and people will try to beat it they might for a short period after that they get caught out but the people who write good quality content will be the winners and keep writing quality content a question might be does google count how many no follows there are i wounder
Search results are presented in an ordered list, and the higher up on that list a site can get, the more traffic the site will tend to receive. For example, for a typical search query, the number one result will receive 40-60% of the total traffic for that query, with the number two and three results receiving significantly less traffic. Only 2-3% of users click beyond the first page of search results.
Just like the new structure on JavaScript links and them now carrying weight and being crawlable, the PageRank sculpting change is understandable. Google now can and wants to index more of the web (JavaScript link change). Google wants to reverse a method that can only help people in the know (PageRank sculpting change). Logically, all is very understandable.
If you’re not getting the clicks… you may need to invest more money per click. As you might expect, there are algorithms in play for SEM. Also, the more you pay, the more likely you are to be served with high-value (in terms of potential spending with your business) clicks. Or, you may just need to re-evaluate your keyphrase – maybe it’s not as popular as the figures, provided by Google Adwords, suggest?

Write a description that would both inform and interest users if they saw your description meta tag as a snippet in a search result. While there's no minimal or maximal length for the text in a description meta tag, we recommend making sure that it's long enough to be fully shown in Search (note that users may see different sized snippets depending on how and where they search), and contains all the relevant information users would need to determine whether the page will be useful and relevant to them.
Google might see 10 links on a page that has $10 of PageRank to spend. It might notice that 5 of those links are navigational elements that occur a lot throughout the site and decide they should only get 50 cents each. It might decide 5 of those links are in editorial copy and so are worthy of getting more. Maybe 3 of them get $2 each and 2 others get $1.50 each, because of where they appear in the copy, if they’re bolded or any of a number of other factors you don’t disclose.
When calculating PageRank, pages with no outbound links are assumed to link out to all other pages in the collection. Their PageRank scores are therefore divided evenly among all other pages. In other words, to be fair with pages that are not sinks, these random transitions are added to all nodes in the Web. This residual probability, d, is usually set to 0.85, estimated from the frequency that an average surfer uses his or her browser's bookmark feature. So, the equation is as follows:
As Rogers pointed out in his classic paper on PageRank, the biggest takeaway for us about the eigenvector piece is that it’s a type of math that let’s you work with multiple moving parts. “We can go ahead and calculate a page’s PageRank without knowing the final value of the PR of the other pages. That seems strange but, basically, each time we run the calculation we’re getting a closer estimate of the final value. So all we need to do is remember the each value we calculate and repeat the calculations lots of times until the numbers stop changing much.”
Matt, you don’t mention the use of disallow pages via robots.txt. I’ve read that PageRank can be better utilised by disallowing pages that probably don’t add value to users searching on engines. For example, Privacy Policy and Terms of Use pages. These often appear in the footer of a website and are required by EU law on every page of the site. Will it boost the other pages of the site if these pages are added to robots.txt like so?
As of 2009, there are only a few large markets where Google is not the leading search engine. In most cases, when Google is not leading in a given market, it is lagging behind a local player. The most notable example markets are China, Japan, South Korea, Russia and the Czech Republic where respectively Baidu, Yahoo! Japan, Naver, Yandex and Seznam are market leaders.
The PageRank theory holds that an imaginary surfer who is randomly clicking on links will eventually stop clicking. The probability, at any step, that the person will continue is a damping factor d. Various studies have tested different damping factors, but it is generally assumed that the damping factor will be set around 0.85.[5] In applications of PageRank to biological data, a Bayesian analysis finds the optimal value of d to be 0.31.[24]
Mathematical PageRanks for a simple network, expressed as percentages. (Google uses a logarithmic scale.) Page C has a higher PageRank than Page E, even though there are fewer links to C; the one link to C comes from an important page and hence is of high value. If web surfers who start on a random page have an 85% likelihood of choosing a random link from the page they are currently visiting, and a 15% likelihood of jumping to a page chosen at random from the entire web, they will reach Page E 8.1% of the time. (The 15% likelihood of jumping to an arbitrary page corresponds to a damping factor of 85%.) Without damping, all web surfers would eventually end up on Pages A, B, or C, and all other pages would have PageRank zero. In the presence of damping, Page A effectively links to all pages in the web, even though it has no outgoing links of its own.
Matt, this is an excellent summary. I finally got around to reading “The Search” by John Battelle and it was very enlightening to understand much of the academia behind what led to the creation of Backrub.. er Google.Looking at how many times the project was almost shutdown due to bandwidth consumption (> 50% of what the university could offer at times) as well as webmasters being concerned that their pages would be stolen and recreated. It’s so interesting to see that issues we see today are some of the same ones that Larry and Sergey were dealing with back then. As always, thanks for the great read Matt!
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