Search results
The percentage shows the perceived importance, and the arrows represent hyperlinks. 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.
Nov 12, 2023 · PageRank is a versatile algorithm that can be applied to various types of graphs. It requires only the graph’s edges to operate, making it a valuable addition to your algorithm toolbox. It is ...
- Polo Chau
Google matrix of Wikipedia articles network, written in the bases of PageRank index; fragment of top 200 X 200 matrix elements is shown, total size N=3282257 (from [1]) A Google matrix is a particular stochastic matrix that is used by Google 's PageRank algorithm. The matrix represents a graph with edges representing links between pages.
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Wikipediais the world’s largest online encyclopedia, comprising millions of pages and links between pages. It also gives away all of this data in an easy-to-process format, via its periodic database dumps. What would happen if we took this set of data and ran the classic PageRank algorithm on every single page? PageRankis a method for determining t...
Top pages
1. The PageRank of a document is the probability that a visitor will end up at that document after uniform random browsing. As such, the sum of all the PageRanks in the set of documents must equal 1. Because probabilities can get very small, the base-10 logarithm of the PageRank will be shown instead of the raw probability. A list of the top 10 000 pages on English Wikipedia, along with the log10 PageRanks in descending order: wikipedia-top-pageranks.txt A treemap of the (linear) probabilitie...
Sorting “What links here”
1. As mentioned in the introduction, my original motivation for computing PageRanks was to sort the “What links here” list of pages by PageRank. For the arbitrarily chosen page Telescope (as of 2014-02-08), here are the links in MediaWiki’s order (with various data cleanup) versus in descending PageRank order. We can see that the original order starts off alphabetically, then becomes a mess of obscure people, places, and concepts with random clusters. By contrast, the PageRanked list shows fa...
Language choice
Handling the Wikipedia page link data set would involve storing and manipulating tens or hundreds of millions of items in memory, so this consideration heavily influenced what programming language I would use to solve the problem: 1. Python (CPython) is slow for arithmetic operations (about 1/30× the speed of C/C++) and is memory-inefficient (e.g. a list of integers uses far more than 4 bytes per element). Python would be usable for this problem only if I used NumPy, which packs numbers in me...
Program architecture
Here is a high-level view of how my program works: 1. Read the file enwiki-yyyymmdd-page.sql.gz, decompress gzip on the fly, parse the SQL text to extract the tuples of (string page title, int page ID), filter out entries that are not in namespace #0, and store them in memory in a Map . 2. Read the file enwiki-yyyymmdd-pagelinks.sql.gz, decompress gzip on the fly, parse the SQL text to extract the tuples, filter out tuples whose target is not a known page or not in namespace #0...
Room for improvement
This program meets my needs for the problem at hand, and it handles the data and most edge cases correctly. However, there’s still room for improvement when it comes to flexibility and time/space efficiency. 1. Instead of using a hand-coded FSM, make the SQL parsing less brittle and code it at a higher level. Perhaps use a real SQL parser library, or at least have a proper lexer and context-free grammar parser. 2. Pack the string/int map into a much more compact format, because this is the bi...
Java source code: 1. WikipediaPagerank.java(main program) 2. Pagerank.java(core numerical algorithm) 3. PageIdTitleMap.java(boring I/O) 4. PageLinksList.java(boring I/O) 5. SqlReader.java (ugly FSM) 6. SortPageTitlesByPagerank.java(secondary program) English Wikipedia data dumps: https://dumps.wikimedia.org/enwiki/ From a snapshot of your choice, d...
Of them, 1,075,990 are living, [36] or 56% i.e. the living outnumber the dead. 213,943 more were living in the Wikipedia period, [37] raising the percentage of this period's people to 67 (two-thirds). 341,079 more were living in the century preceding Wikipedia (1901–2000), [38] raising the percentage of the contemporaries to 85. That is, people who lived at some time from the beginning of ...
Sep 26, 2023 · As the PageRank algorithm evolved, it became the backbone of a new search engine called Google, which Page and Brin launched in September 1998. The early success of Google can be primarily attributed to the effectiveness of the PageRank algorithm in delivering superior search results compared to other search engines.
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256. Stanford, CA. 1996-1998. BackRub was "web crawler" software "to traverse the web" created by Larry Page and Sergey Brin starting in 1996 while they were PhD students at Stanford University. Early Google logo from 1998. Larry Page and Sergey Brin in the garage of a Menlo Park, CA home where they first set up shop as "Google".
