The PageRank Citation Ranking: Bringing Order to the Web
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1 The PageRank Citation Ranking: Bringing Order to the Web Marlon Dias Information Retrieval DCC/UFMG
2 Introduction Paper: The PageRank Citation Ranking: Bringing Order to the Web, 1999 Authors: Lawrence Page, Sergey Brin, Rajeev Motwani, Terry Winograd Page and Brin were MS students at Stanford They founded Google in September, 98. Most of this presentation is based on the original paper (link)
3 The Initiative's focus is to dramatically advance the means to collect, store, and organize information in digital forms, and make it available for searching, retrieval, and processing via communication networks -- all in user-friendly ways. Stanford WebBase project
4 Motivation Web is vastly large and heterogeneous Original paper's estimation were over 150 M pages and 1.7 billion of links Pages are extremely diverse Ranging from "What does the fox say? " to journals about IR Web Page present some "structure" Pagerank takes advantage of links structure
5 Motivation Inspiration: Academic citation Papers are well defined units of work are roughly similar in quality are used to extend the body of knowledge can have their "quality" measured in number of citations
6 Motivation Web pages, on the other hand proliferate free of quality control or publishing costs huge numbers of pages can be created easily artificially inflating citation counts They vary on much wider scale than academic papers in quality, usage, citations and length
7 Motivation A random archived message posting asking an obscure question about an IBM computer is very different from the IBM home page A research article about the effects of cellphone use on driver attention is very different from an advertisement for a particular cellular provider
8 The average web page quality experienced by a user is higher than the quality of the average web page. This is because the simplicity of creating and publishing web pages results in a large fraction of low quality web pages that users are unlikely to read.
9 Idea Creates a graph based on link structure Pages are nodes Links are edges A C A C Forward links are outedges B B Backlinks are inedges A and B are backlinks of C
10 Assumptions Link from page A to page B is a vote from A to B Highly linked pages are more important than pages with few links Backlinks from high PR-pages count more than links from low PR-pages combination of PR and text-matching techniques result in highly relevant search results
11
12 Definition Simplification of Pagerank A simple ranking function u is a web page F u is a set of pages that u points B u is the set of pages pointing to u c is a normalization factor N u = F u
13 Definition Rank of a page is divided among its forward links Equation is recursive May be computed by starting with any set of ranks it iterates until it converges.
14 Definition Problem with previous equation: Consider two web pages that point to each other but to no other page. Suppose there is some web page which points to one of them. During iteration, this loop will accumulate rank but never distribute any rank Since there are no outedges.
15 Definition To overcome the problem: where E(u) is come vector over the web pages. Based on a random surfer model.
16 Definition Finally, Pagerank is usually defined as:
17 Definition Finally, Pagerank is usually defined as: represents the change to get to page u from any other page (random walk)
18 Definition Finally, Pagerank is usually defined as: represents the change to get to u from pages that points to u, B u.
19 Example Page A 1 Page B 1 Page A 1.49 Page B 0.78 ~ 20 iterations Page C 1 Page C 1.58 Page D 1 Page D 0.15 Complete iteration process can be found here (made by Alberto Ueda).
20 Convergence Pretty quick and robust!
21 Paper Implementation Repository size: 24M web pages (over 75M unique URLs) computing PR of entire repository takes ~5h Issues: Volume incorrect HTML dynamics of the web, page exclusion (robots.txt)
22 Usage Search combination of retrieve models and pagerank for ranking Commercial Interests It is not easily manipulated Estimation of Web Traffic Corresponds to a random web surfer Backlink predictor for crawling
23 Unwanted usage Bmw.de banned from Google in early 2016 due to doorway page (link) Google bomb President article (2007) Repub & Dem article (2017)
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