1. Presented by : Manoj Kumar & Harsha Vardhana Impact of Search Engines on Page Popularity by Junghoo Cho and Sourashis Roy (2004)

2. Overview Introduction Page Rank & Popularity Popularity Evolution - Experimental study - Theoretical study Interesting Facts Conclusion

3. “If your page is not indexed by Google, your page does not exist on the web” How do search engines rank web pages for a given query? Judgment of “quality” and “relevance”… The “rich-get-richer” phenomenon

4. Page rank & Popularity Number of links to a page Weighted links PR(p i )=d + (1 - d)[PR(p 1 )/c 1 + …. + PR(p m )/c m ] Popularity means… formula taken from the paper

5. A sample query Suppose we have a query “The Bugatti Veyron” Page I built out of interest: http://cs.odu.edu/~vmadduri/bugatti/ http://cs.odu.edu/~vmadduri/bugatti/

6. How the popularity of web pages evolve?

7. A setup for experiment Downloaded pages of 154 web sites Downloaded pages until either no more pages were in reach or up to a maximum of 200,000 Downloaded 5 million pages(nodes) but the nodes in the web graph of the snapshots are around 13 million to 15 million. (In a GRAPH : node=> web page ; edge=> outgoing link) Common pages in both the snapshots count up to 7.8 million. Initial page rank assumed as 1.

8. Sample snapshot (web pages and corresponding links) The nodes in the graph drawn may be more than the nodes in the snapshot

9. Two snapshots S 1 & S 2 in a gap of 7 months s1 contains 13 million nodes and s2 contains 15 million nodes (since we are interested in the popularity of a page, only the common nodes, which is 7.8 millions, are considered) Using Incoming Links (IL) Total incoming links to a group are : Increase in popularity of a group : IL(G i, S 2 ) – IL(G i,S 1 ) formula taken from the paper Experimental study

10. Results (when used IL as metric) All results taken from the paper

11. Results cont. (detailed view)

12. Result for relative increase in popularity

13. Page rank as Metric Two snapshots S 1 & S 2 in a gap of 7 months Using Page Rank (PR) Page Rank of a group :

14. Results using PR as metric

15. Results cont. (detailed view)

16. Results for relative increase of page rank

17. If search engines do not rank pages based on the current popularity, will popular pages still get more popular?

18. Theoretical Study Random Surfer Model Search Dominant Model

19. Random Surfer Model Popularity P(p,t) Visit popularity V(p,t) = r1 P(p,t)

21. Google Popularity Evolution

22. Popularity Evolution in search dominant model Proposition : Under the search-dominant model, the number of visits to page pat time t satisfies the following equation: V(p,t) = r2 P(p,t) ^ (9/4) where r2 is a normalization constant.

23. A test result

24. Closer look at the result

25. This is TRUE! Result predicts that it takes 66 times longer under the search-dominant model than under the random-surfer model in order for a page to become popular!

26. Observing all the results we should believe that search engine ( an indispensable tool ) plays a significant role in the survival of a web page. Once a page gets a reasonable ranking in search dominant model the popularity increases very quickly.

27. Share Of Searches: July 2006 http://searchenginewatch.com/showPage.html?page=2156451

28. Can a competitor harm others sites ranking in Google?

29. Conclusion Popular pages are getting more popular. Unpopular pages are getting relatively less popular. Many high quality pages are ignored since no one discovered them yet. There is an urgent need to develop a new ranking mechanism.