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Paim & Figueiredo - 2011
Measuring Hyperlink Distances Wikipedia case study Rodrigo R. Paim - - PowerPoint PPT Presentation
Measuring Hyperlink Distances Wikipedia case study Rodrigo R. Paim - - PowerPoint PPT Presentation
Measuring Hyperlink Distances Wikipedia case study Rodrigo R. Paim Daniel R. Figueiredo Universidade Federal do Rio de Janeiro 2 nd Workshop of Brazilian Institute for Web Science Research Webpages and Hyperlinks Paim & Figueiredo - 2011
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Paim & Figueiredo - 2011
The Web Graph
Webpages → Vertices Hyperlinks → Directed Edges
UFRJ Campus Rio Brazil
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Webpages have some specific content
Can’t get it from structure of the web
New concept to analyze “connected webpages”
Inversely proportional to contextual similarity
Paim & Figueiredo - 2011
Hyperlink Distance
Physics Maths Economic Crisis Europe Greece d1 d2 d3
d1 < d2, d3
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Paim & Figueiredo - 2011
Measuring Distances
Multiple distance metrics
Variation of Jaccard distance
IDF-based
Keywords play a crucial role
Indicate context of a webpage
Why to measure distances?
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Paim & Figueiredo - 2011
Image from The Opte Project Website
Can one go from any webpage to another using local information only?
Navigating the Web
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Paim & Figueiredo - 2011
Navigating the Web
Food Car Safety
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Paim & Figueiredo - 2011
Navigating the Web
Food Food Industry Agriculture Restaurant Car Safety
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Paim & Figueiredo - 2011
Navigating the Web
Food Food Industry Manufacturing Car Safety Agriculture Restaurant Nestlé Food Processing
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Paim & Figueiredo - 2011
Navigating the Web
Automotive Industry Food Food Industry Manufacturing Car Safety Automobile Agriculture Restaurant Nestlé Food Processing
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Paim & Figueiredo - 2011
Navigating the Web
Automotive Industry Food Food Industry Manufacturing Car Safety Automobile Agriculture Restaurant Nestlé Food Processing
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Paim & Figueiredo - 2011
Problem Formulation
Decentralized greedy algorithm From any u to any v Choose closest hyperlink to destination Local information only Can it reach destination? In few steps Using hyperlink distances
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Paim & Figueiredo - 2011
Case Study
Wikipedia Two major sets
D: Documents (articles) C: Categories
Keywords of documents
Wikipedia web graph
Vertices (~ 3.6 M) Edges (~ 100 M)
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Paim & Figueiredo - 2011
Navigation Algorithms
BFS
Minimum distance with global knowledge
Random Walk
Next webpage chosen randomly
Greedy Algorithm
Only closest neighbor (dead ends)
Modified Greedy
Closest neighbor (not visited yet)
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Results
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Results
96.45%
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Results
16.88%
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Results
5.46% Dead Ends
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Results
45.27%
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Paim & Figueiredo - 2011
Conclusion
Greedy performs worse than Random Walk But Modified Greedy performs better
Only for big distances However far from optimal (BFS)
Categories are not a good “compass” Ongoing work:
How to define a better greedy algorithm?
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Paim & Figueiredo - 2011