1. A Comparative Study of Link Analysis Algorithms
Presented by,
Chamath Palihawadana
Guhanathan Poravi

2. I am Chamath Palihawadana
Hello!
I am Chamath Palihawadana
Department of Software Engineering
Informatics Institute of Technology
Sri Lanka
Contact:
LinkedIn: chamathpali

3. Introduction to Link Analysis Algorithms
Agenda
Introduction to Link Analysis Algorithms
Overview of algorithms available
PageRank
HITS
Other algorithms
Comparison
Conclusion and Motivation

4. 1. Introduction to Link Analysis algorithms

5. Link Analysis algorithms?
Connectivity
Link structure in WWW
Measure the relationship between nodes
Information retrieval

6. Where is Link analysis algorithms used
Search Engines
Content based websites

7. 2.
PageRank

8. PageRank Algorithm - Background
Presented by Google Founder Larry Page and Sergey Brin (1996)
Inspired by academic citation ranking
Earlier versions of Google used PageRank
Ranking over 150 Million websites

9. Only Looks at the link structure towards a page
PageRank Algorithm
Only Looks at the link structure towards a page
Content has no effect
Backlinks and outbound links
Rank will be high if more backlinks are there

10. Link structure
Backlinks and out links

11. PageRank Algorithm

12. PageRank Algorithm – Advantages and Disadvantages
Low query time
Efficient
Feasible
Disadvantages
Less relevancy
Rank sink
Dead ends
Spider traps (linked)
New pages affect ranking

13. 3.
HITS Algorithm

14. HITS (HYPERTEXT INDUCED TOPIC SEARCH)
Known as hubs and authorities
Considers link structure and content of the page
Two types of pages are involved
Authority – Trustworthy information
Hubs – Keep links to the authorities

15. Hubs and Authorities
A good authority page is pointed by good hubs and a good hub has links to many good authorities.

16. HITS Algorithm
Authority Score calculation
Hubs Score calculation

17. HITS Algorithm – Advantages and Disadvantages
More Relevancy
Combined with other ranking algorithms
General algorithm
Valid calculations
Disadvantages
More query time
Topic drift
Less feasible
Irrelevant authorities
Irrelevant hubs

18. 4.
Other Algorithms

19. 4.1 F-Rank – Feature ranking
Uses PageRank algorithm
Machine learning techniques are used
Page popularity is considered
Domain scoring

20. Uses PageRank like algorithm Used in finding experts
4.2 ExpertiseRank
Uses PageRank like algorithm
Used in finding experts
Experts are weighted
This algorithm generates a measure with how many people who answered and to whom answered.

21. Uses PageRank algorithm Solves the spam issues
4.4 Trust Rank
Uses PageRank algorithm
Solves the spam issues
Quality of a page is valued
Differentiate good sites vs bad sites
TrustRank incorporates the knowledge of the quality of a page and it’s meant to differentiate good and bad sites.

22. More relevant pages will be used Base-set downsizing
4.5 Improved HITS
Uses HITS algorithm
More relevant pages will be used
Base-set downsizing
Differentiate good sites vs bad sites
Projection method – Which projects eigenvectors at the root set of pages, so that most of the pages will be relevant to the original topic
Base-set downsizing – Pages without links to multiple pages will be filtered out. This will cut down the cost of computation effectively.

23. 5.
Comparison

24. Comparison of link analysis algorithms
Only two major algorithms
PageRank
HITS
PageRank
Only web structure
Low Relevancy
Highly Feasible
Crawl time – Efficiency
More popular
HITS
Web structure and content
High Relevancy
Less Feasible
Less Efficient – Query time
Moderate with modified

25. 6. Conclusion and Motivation

26. Conclusion and Motivation
Both PageRank and HITS are proved to be stable
PageRank algorithm is more efficient since the query time does not do any operations
Selection of algorithm is based on the purpose
Motivation – Part of the research on Finding Experts in Q&A platforms and ranking them

27. Thank You!
Any questions?
Contact: