The PageRank algorithm is a fundamental part of Google’s ranking system, developed by Larry Page and Sergey Brin, the founders of Google. It is a mathematical formula used to rank websites in Google’s search engine results pages (SERPs) based on the quantity and quality of inbound links.
Here’s how the original PageRank algorithm worked:
1) It assigned a numerical weighting to each webpage based on the number and quality of links pointing to it.
2) The underlying assumption was that more important pages are likely to receive more links from other sites.
3) It also factored in the PageRank of the linking pages – links from high-ranking pages carried more weight.
4) Pages with higher PageRank scores were deemed more important and ranked higher in SERPs.
However, over time, Google has evolved the PageRank algorithm significantly to combat webspam and improve overall ranking quality. Some key changes include:
1) Link quality over quantity – Links from high-quality, relevant sites matter more than sheer link volume.
2) Penguin Update (2012) – Penalized sites engaged in manipulative link schemes like buying links.
3) Topical relevance – Links from topically related pages are weighted more heavily.
4) User signals – Factors like click data, bounce rates, dwell time are folded into rankings.
5) Machine learning – AI models can evaluate links, content quality, user satisfaction signals, etc.
While the original PageRank concept of using links as a proxy for importance still applies, Google now uses a much more sophisticated machine learning model called RankBrain that combines link signals with vast other ranking factors and data.
The reliance solely on static link scoring has diminished over time. Modern PageRank is one part of a core algorithm that analyzes links, on-page factors, user signals, spam detection, entity relationships, and myriad other signals using AI techniques.
So while the PageRank naming convention persists, the algorithm has evolved significantly from its original simple link popularity model into using complex machine learning for ranking pages in Google’s pursuit of delivering the best, most relevant search results.