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Enhancing Web Search with Hyperlink Analysis and HITS Algorithm

This paper presents an in-depth exploration of hyperlink analysis, specifically using the Hyperlink-Induced Topic Search (HITS) algorithm, to tackle challenges in efficient web crawling and ranking. The research focuses on the significance of web link analysis for improving search engine performance by classifying web pages into hubs and authorities. It discusses the advantages and limitations of the HITS algorithm, suggesting further research into relevance scoring methods and co-citation graphs to enhance search precision. The insights provided may be crucial for developing next-generation web search technologies.

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Enhancing Web Search with Hyperlink Analysis and HITS Algorithm

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  1. Hypersearching the Web, Chakrabarti, Soumen Presented By Ray Yamada

  2. Overview • Why Do We Care? • Purpose of The Paper? • Solution by Clever Project • Pros / Cons of the Paper • Further Research

  3. Why Do We Care? • Web Link Analysis is crucial for efficient Crawling and Ranking algorithms • Crawling: Google Sitemap Submission, Yahoo Directory • Ranking: Relevant Result

  4. Purpose of The Paper? • To Overcome These Challenges: • Its Size & Growth • Its Content Types • Language Semantics • New Language • Staleness of Results • SPAM • And More…

  5. Solution: Hyperlinks, Hyperlinks, Hyperlinks… • Can Think of the Web as a Directed Graph • Node = Web page (URL) • Edge = Hyperlink

  6. Solution: HITS Algorithm • Hyperlink-Induced Topic Search (HITS) • A.k.a. Hubs and Authorities • Hubs – Highly-valued lists for a given query • Ex. Yahoo Directory, Open Directory Project and Bookmarking sites. • Authorities – Highly endorsed answers to the query • Ex. New York Times, Huffington Post, Twitter • It is possible for a webpage to be both Hub and Authority • Ex. Restaurant Review Blogs

  7. Solution: HITS Algorithm Cont… • For each page p, we assign it two values hub(p) and auth(p) • Initial Value: For all p, hub(p) = 1, auth(p) = 1 (or any predetermined number) • Authority Update Rule: For each page p, update auth(p) to be the sum of the hub scores of all pages that point to it. • Hub Update Rule: For each page p, update hub(p) to be the sum of the authority scores of all pages that point to it. • Normalize and Repeat

  8. Solution: HITS Algorithm Cont… Calculation

  9. Pros: • Accurately addresses concerns and challenges we currently deal with • Great introduction to search engine algorithm • Briefly covered many topics (Breadth)

  10. Cons: • Some materials are out of date (1999) • Ex. Google vs. Clever Project • Lack of Depth • Ex. Normalization of Hub and Auth values

  11. Further Research: HITS Algorithm – Extreme Cases • Large-in-small-out sites • High Auth(p) • No Problem • Small-in-large-out sites • High Hub(p) • Problem

  12. Further Research: HITS + Relevance Scoring Method • Vector Space Model (VSM) • Documents and queries are represented by vectors • Term Frequency • Okapi Measurement • Term Frequency + Document Length • Cover Density Ranking (CDR) • Phrase Similarity (How close terms appear)

  13. Further Research: HITS + Relevance Scoring Method • Use Cosine Relevance Test Price Car

  14. Further Research: HITS + Relevance Scoring Method • Three-Level Scoring Method (TLS) • Manual Evaluation of Relevance • Relevant Links = 2 points • Slightly Relevant Links = 1 point • Inactive Links + Error Links (404, 603) = 0 point • Irrelevant Links = 0 point • Order of query terms matters

  15. Further Research: Co-citation Graph • Regular Link Graph: • Co-citation Graph:

  16. What’s Next? • Google’s New Search Index: Caffeine • Announced June 8th, 2010 • Up to 50% fresher results • Twice as fast • Real Time Search • Twitter / Facebook http://googleblog.blogspot.com/2010/06/our-new-search-index-caffeine.html

  17. References • Chakrabarti, Soumen; Dom, Byron; Kumar, S. Ravi; Raghavan, Prabhakar; Rajagopalan, Sridhar & Tomkins, Andrew. (1999). "Hypersearching the Web" [Article]. Scientific American, June1999, ():.  • Longzhuang Li , Yi Shang , Wei Zhang, Improvement of HITS-based algorithms on web documents, Proceedings of the 11th international conference on World Wide Web, May 07-11, 2002, Honolulu, Hawaii, USA  [doi>10.1145/511446.511514] • Henzinger, M. (2001). Hyperlink analysis for the Web. IEEE Internet Computing, 5(1), 45-50. • Kleinberg, Jon (1999). "Authoritative sources in a hyperlinked environment" (PDF). Journal of the ACM46 (5): 604–632. doi:10.1145/324133.324140. • von Ahn, Luis (2008-10-19). "Hubs and Authorities" (PDF). 15-396: Science of the Web Course Notes. Carnegie Mellon University. Retrieved 2008-11-09.

  18. Q & A

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