Analysis of Link Structures on the World Wide Web and Classified Improvements

1. Analysis of Link Structures on the World Wide Web and Classified Improvements Greg Nilsen University of Pittsburgh April 2003

2. The Problem • The web is a complex, unorganized structure. • Search engines can be fooled: • Search Engine Designers v. Advertisers • User feedback rarely used to quantify results.

3. Outline • Background • Kleinberg’s Algorithm • The Idea • Implementation • Results and Conclusions • References

4. Background – Kleinberg’s Algorithm • Basic Idea: • Create a Focused Subgraph of the Web • Iteratively Compute Hub and Authority Scores • Filter Out The Top Hubs and Authorities • Extended Ideas: • Similar Page Queries • Non-Principal Eigenvectors

5. Background – Kleinberg’s Algorithm • Create a focused subgraph of the web (a base set of pages) • Why? • We need a set that is: • Relatively Small • Rich in Relevant Pages • Contains Most of the Strongest Authorities

6. Background – Kleinberg’s Algorithm • Start with a root set: • In our case we are using a data set that started with the first 200 results of a text-based search on AltaVista. • Create our base set: • Add in all pages that link to and from any page in the root set.

7. Background – Kleinberg’s Algorithm Root

8. Background – Kleinberg’s Algorithm Root

9. Background – Kleinberg’s Algorithm Root

10. Background – Kleinberg’s Algorithm Root

11. Background – Kleinberg’s Algorithm Base Root

12. Background – Kleinberg’s Algorithm • Now that we have a focused subgraph, we need to compute hub and authority scores. • Start by initializing all pages to have a hub and authority weights of 1. • Compute new hub and authority scores: • Hub Score = Σ (Authority Scores of All Pages The Hub Points At) • Authority Score = Σ (Hub Scores of All Pages That Point to the Authority)

13. Background – Kleinberg’s Algorithm • Normalize the new weights (hubs and authorities separately) so that the sum of their squares is equal to one. • Repeat the computing of weights and their normalization until the scores converge (usually 20 iterations). • When we have completed computing the hub and authority scores, we then take the top authority scores as our top results.

14. Background – Kleinberg’s Algorithm • Similar page queries • Once we produce results, a searcher may wish to find pages similar to a given result. • In order to do this, we can use the algorithm that we have discussed above. • This time, we build a root set of the pages that point to the given page. • We then grow this into a base set and determine the hubs and authorities for the new set. • This will result in pages similar to the initial page.

15. Background – Kleinberg’s Algorithm • Non-Principal Eigenvectors • An eigenvector is a densely linked collection of hubs and authorities within the subgraph. • In the Kleinberg algorithm, we produce the principal eigenvector by iteratively computing hub and authority scores until convergence. • However, the principal eigenvector may not contain all of the information desired by the search.

16. Background – Kleinberg’s Algorithm • Example: A search for “jaguar” • This search will produce 3 strong eigenvectors due to different meanings of the word: • Jaguar – the car • Jaguar – the cat • The Jacksonville Jaguars NFL team • Which one of these will be returned as the principal eigenvector depends heavily on the initial set of pages. • We cannot determine which of the three meanings that the searcher meant.

17. Background – Kleinberg’s Algorithm • Therefore, we can produce results that come from “strong” eigenvectors. • However, we can still miss relevant pages. • For example, the search for “WWW conferences” produces the most pertinent results on the 11th non-principal eigenvector. • How to determine relevant eigenvectors is a topic that is still currently under research.

18. The Idea • Kleinberg’s algorithm produces “good” results, but subject to a phenomena known as “topic drift”. • The hub weights of some sites such as yahoo.com or eBay.com cause irrelevant clusters to be identified as major eigenvectors. • So, while structural information provides us with much information about a query, additional information seems necessary.

19. The Idea • Kleinberg’s algorithm also uses only the top authority scores, but there may be useful pages that rank strongly as hubs. • Since web queries are an application driven towards maximizing user satisfaction, we can use user feedback to try and weight hub and authority scores so that we can classify “better” results using SVMs.

20. The Idea A plot of hub vs. authority scores. Hubs Authorities

21. The Idea Hubs the dividing hyperplane Authorities

22. The Idea • We can then compile data from different types of searches, we may be able to generalize this hyperplane so that we pull more relevant results from the result of Kleinberg’s algorithm.

23. Implementation • Start with data from the University of Toronto’s Link Analysis Ranking Algorithm repository. • Getting results for a text-based search engine is very difficult any more now that search engines have gotten smarter. • Contains data for 8 distinct types of searches.

24. Implementation • Next, we implement Kleinberg’s algorithm in C++ that reads in the datasets and outputs a web page with the top 50 hubs and top 50 authorities on the page. • Compile a survey in which participants are asked if a result is useful for a mixture of the top 25 hubs and top 25 authorities for the search on “abortion” (a search that tends to produce two distinct groups) and “genetic” (a search that is more generic in nature).

25. Implementation • Using the results of the survey, determine a class label (1 or 0) for each result. • With the resulting labels, perform learning via SVMs in Matlab using the hub and authority scores as input and the class label as output.

26. Implementation • Using the weights resulting from the SVM learning and plug them into our initial program to compute SVMscores for all web pages. • Sort the web pages based on their SVMscores and output the top 50 results to a web page.

27. Results • Mean Misclassification Rates For Entire Dataset of “genetic” and “abortion” Terms: • Training = 0.3922 • Testing = 0.4063 • Mean Misclassification Rates for the Same Dataset Renormalized Against Largest Value in the Vector: • Training = 0.3418 • Testing = 0.3345

28. Results • Mean Misclassification Rates for the “abortion” Dataset: • Training = 0.1862 • Testing = 0.1888 • Mean Misclassification Rates for the Renormalized “abortion” Dataset : • Training = 0.1878 • Testing = 0.1850

29. Results • Mean Misclassification Rates for the “genetic” Dataset: • Training = 0.3530 • Testing = 0.3566 • Mean Misclassification Rates for the Renormalized “genetic” Dataset : • Training = 0.3548 • Testing = 0.3463

30. Results • Mean Misclassification Rates for Individuals:

31. Results • Mean Misclassification Rates Overall Dataset Expanded To Include (Auth/Hub, Auth2, Hub2, Auth*Hub): • Training = 0.2886 • Testing = 0.2903 • Mean Misclassification Rates for the Renormalized “genetic” Dataset : • Training = 0.2848 • Testing = 0.2861

32. Conclusions • While the current results provide significant improvement on some searches in our datasets, for some searches the results are not much of an improvement. • This may be due to the fact that user feedback was limited.

33. Conclusions • We have shown that generalization in terms of the entire dataset and on a per person basis do not provide good results. • Too many factors being combined with too few features. • We have also demonstrated that generalization on a per search basis and the use of expanded datasets may provide for better learning of user preferences of results.

34. References • J. Kleinberg. Authoritative sources in a hyperlinked environment. Journal of ACM (JASM), 46, 1999. • A. Borodin, G. Roberts, J. Rosenthal, P. Tsaparas. Finding authorities and hubs from link structures on the world wide web. Proceedings of the 10th International World Wide Web Conference, 2001. • S. Brin and L. Page. The anatomy of a large-scale hypertextual Web search engine. Proc. 7th WWW Conf., 1998. • R.A. Botafogo, E. Rivlin, and B. Shneiderman: Structural Analysis of Hypertexts: Identifying Hierarchies and Useful Metrics. ACM Transactions on Information Systems, Vol. 10, No. 2. ACM, 1992. pp. 142-180 • J. Carrire and R. Kazman. WebQuery: Searching and Visualizing the Web through Connectivity, in Proceedings of WWW6 (Santa Clara CA, April 1997). • E. Garfield. Citation analysis as a tool in journal evaluation. Science, 178:471--479, 1972.

35. References • L. Katz. A new status index derived from sociometric analysis. Psychometrika, 18:39-43, 1953. • G. Pinski and F. Narin. Citation influence for journal aggregates of scientific publications: Theory with application to literature of physics. Information Processing & Management, 12:297--312, 1976. • C.H. Hubbell. An input-output approach to clique identification. Sciometry 28, 377-399, 1965.