PageRank formula: PR(A) = (1-d) + d Σ(PR(C)/L(C))
PageRank
PageRank formula: PR(A) = (1-d) + d Σ(PR(C)/L(C))
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: PageRank works by counting the number and quality of links to a page to determine a rough estimate of how important the website is. The underlying assumption is that more important websites are likely to receive more links from other websites. Currently, PageRank is not the only algorithm used by Google to order search results, but it is the first algorithm that was used by the company, and it is the best known. As of September 24, 2019, all patents associated with PageRank have expired.
Example
If a web page A has 3 incoming links from web pages C1, C2, and C3, with PageRanks of 0.5, 0.3, and 0.2 respectively, and C1, C2, and C3 have 1, 2, and 3 outgoing links respectively, then the PageRank of A would be calculated as follows: PR(A) = (1-d) + d [(0.5/1) + (0.3/2) + (0.2/3)], assuming a damping factor d of 0.85.
Understanding the PageRank formula is crucial for webmasters and SEO professionals as it helps them optimize their websites for better search engine rankings.
Related concepts
Cross-entropy
Cross-entropy loss equation: H(p, q) = -Σ(p(x) * log(q(x)))
Jensen–Shannon divergence
Jensen-Shannon divergence formula: D_JS(P||Q) = 1/2 * D_KL(P||(M)) + 1/2 * D_KL(Q||(M))
Bayes' theorem
Bayes' theorem formula: P(A|B) = [P(B|A) * P(A)] / P(B)
Write the triplet loss formula: max(d(a,p) - d(a,n) + margin, 0)
Triplet loss formula: max(d(a,p) - d(a,n) + margin, 0)
Entropy (information theory)
H(X) = −∑x∈X p(x) log(p(x))
Chain rule
Chain rule formula: h'(x) = z'(y(x)) * y'(x)
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