Beginner Topic 12 of 51 Emerging & Frontier Topics
PageRank
Ranking pages by link structure.
How it works
PageRank, developed by Larry Page and Sergey Brin, scores a page by the importance of the pages linking to it. Imagine a random surfer who follows a random link with probability d, usually 0.85, and otherwise jumps to a random page; a page's rank is the long-run fraction of time spent there. Mathematically it is the dominant eigenvector of the link matrix, computed by power iteration: start with equal scores and repeatedly redistribute each page's rank evenly across its outgoing links until values settle. The damping factor handles dead ends and loops. A link from a high-rank page counts for more than many links from low-rank pages.
Where it is used
It originally powered Google's ranking and the idea is used in citation analysis, social network influence and recommendation.
Common pitfall
Link farms and paid links manipulate it, and today it is only one of many signals. Treat it as an importance score on a graph, not a measure of content quality.
Where it fits in the course
PageRank is topic 12 of 51 in Emerging & Frontier Topics, rated Beginner. It follows Search Engines. The next topic is Recommender Systems.
Practice and explore
- Open the interactive lesson, with a mini quiz.
- Take the Emerging & Frontier Topics quiz.
How to study this topic
- Read the explanation above and restate the definition in your own words.
- Open the interactive lesson and answer its mini quiz.
- Read the common pitfall, then write a short example that avoids it.
- Check your understanding in the Emerging & Frontier Topics quiz, and revisit the topic if you miss a question.
Frequently asked questions
What is PageRank?
Where do people use PageRank?
What is the most common mistake with PageRank?
What should I learn before this topic?
How can I practice it?
Is PageRank hard to learn?
How long does it take to learn PageRank?
Which topics are related to PageRank?
What should I study after PageRank?
Related topics
- Search Engines (Emerging & Frontier Topics)
- Recommender Systems (Emerging & Frontier Topics)
- Information Retrieval (Emerging & Frontier Topics)
- Data Science Workflow (Emerging & Frontier Topics)