The PageRank Citation Ranking “Bringing Order to the Web”

1. The PageRank Citation Ranking“Bringing Order to the Web”

2. Google Design Goals • Scale up to work with much larger collections • In 1997, older search engines were breaking down due to manipulations by advertisers, etc. • Exhibit high precision among top-ranked documents • Many documents are kind-of relevant • For the Web, relevant should be just the very best documents • Provide data sets for academic research on search engines

3. System Anatomy

4. Basic Concept • Bibliometrics • Use citation patterns like for academic research • But Web documents are not reviewed • Web documents do not have the same cost of production/publishing • Programs can easily generate pages pointing to another page • Very valuable to attempt to game the system

5. PageRank Design • PageRank • Rank importance of pages • Each incoming link raises importance • Importance increment related to rank of source page • Importance increment normalized for number of links on source page • A high ranking results from • Lots of links from low ranked pages • Fewer links from highly ranked pages

6. Ranking Process • Initialize PageRank values based on heuristics Initial values do not change results, only time to converge • Repeat until the rankings converge: For each incoming link L PageRank = PageRank + Rank ( Source (L) ) #Links ( Source (L) ) Model Web user with some chance of jumping to random location PageRank = c * PageRank

7. Examples

8. Problem with Subgraphs • The Web is composed of many independent graphs • Links among themselves but no links in/out of the set • This results in ranking between independent graphs to be difficult • Solution • Introduce a decay factor • This also increases the rate of convergence

9. Problem with Dangling Links • Many links go to pages with no outgoing links • Influence the distribution of “rank” • Don’t know how to push their rank back into system during iteration • They are to pages that • Have no links • Have not been downloaded • Are not in a form that the system can identify • There are lots of them • Solution • Remove “dead-ends” during convergence • Compute ranks of these after convergence

10. Implementation • Web issues • Infinitely large Web sites, pages, and URLs • Much broken HTML • Web is constantly changing • PageRank Implementation • Each URL assigned integer ID • Dangling links iteratively pruned from graph • Few iterations get rid of most • Generate guess at ranking • Does not affect outcome (much), just how fast it converges • Iterate rank computation until convergence • Add dangling links back in • Iterate rank computation again for the same number of times that it took for dangling links to be removed

11. Convergence Scaling • Scales well • 161 million links require 45 iterations • 322 million links require 51 iterations

12. Searching with PageRank • Google search • Uses a variety of factors • Standard IR measures • Proximity • Anchor Text • PageRank • PageRank most valuable for underspecified queries (e.g. few terms, lots of results) • “Spam pages” given no/low PageRank to reduce their effect on resulting weights.

13. PageRank with Title Search AltaVista Simple title search with PageRank

14. PageRank Applications • Estimating WebTraffic • Because it models a random Web surfer • Backlink Predictor • Like to estimate number of backlinks to identify important pages • Use CPU/bandwidth to increase precision • Use incomplete data to rank pages to generate order of importance for crawling • User Navigation • Browser can annotate link based on PageRank to provide user clue as to destination’s value