Presentation for the project by: Anhad Jai Singh Jigar Kaneriya Rishi Mittal Sandeep Reddy Biddala Source code: https://bitbucket.org/pagerank/lapagerank/ Website: http://web.iiit.ac.in/~rishi.mittal/ire/

1. Local Approximation
of Page Rank
Rishi Mittal
Jigar Kaneria
Anhad Jai Singh
Sandeep Reddy Biddala

2. What is PageRank?
• PageRank is a ranking algorithm first devised by Google's
founder Larry Page
• One of the most influential algorithms of the previous decade.
• Revolutionized ranking and it's application in IR made the
Search Engine giant it is today.

3. What does it do?
How does it work?
• PageRank is a link analysis algorithm that essentially
operates on the webgraph.
• PageRank works by counting the number and quality of
links to a page to determine a rough estimate of how
important the website is.
• The underlying assumption is that more important websites
are likely to receive more links from other websites.

4. Drawbacks of PageRank
• Page rank is a very compute intensive algorithm.
• In essence PageRank does matrix multiplication on huge matrix
sizes, this requires large amounts of computation resources.
• Best possible way to do matrix multiplication is power
exponentiation, but given the size of the webgraph (or even a
smaller corpus like wikipedia) the compute time still runs into hours
depending on the workstation.
• Every time a new page/node is introduced into the graph, the entire
computation needs to be re-run to get the new precise PageRank.

5. What is the Local Approximation of
PageRank? Why do we need it?
• Considering all the disadvantages of the original PageRank
algorithm a number of improvements for it have been proposed
over the years.
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• We use a local approximation technique to drastically reduce the
computation time while still keeping the results accurate and
relevant within a certain degree.
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• Instead of computing the PageRank of a page after traversing the
entire webgraph, we iterate over a sub-section of it and
approximate the PageRank. This approximation is called the Local
Approximation of the PageRank.

6. Advantages
• Drastically reduced computation time.
• Reduced compute power requirements implies it can be run
on a regular workstation and still generate respectable results.
• If and when a new node/page is introduced into the webgraph
it's PageRank can be quickly computed by a local
approximation rather than waiting for the entire PageRank
computations to finish.
This allows for faster indexing and retrieval, thus giving us a
fresher index.

7. Drawbacks
• Results can vary drastically depending on the density of links in
the webgraph being processed.
• The PageRank computed is an approximation although the
accuracy of the results can be improved if the the radius of the
sub-graph being considered for approximation is increased.
• Increasing the radius of the subgraph yields diminishing
returns as compared to the increased compute power being
added.

8. Statistics
PageRankLocal Approximation

9. Comparison of results

10. Screenshots
The Local Approximation PageRank algorithm is a backend feature and thus is not
directly visible to nor interacts with the end user.
Our code is designed to be integration friendly rather than for stand-alone usage by
the layman.
As a consequence of this we provide a cli interface rather than a un-integratable GUI.

11. Documentation and Code
Source code: https://bitbucket.org/pagerank/lapagerank/
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"Talk is cheap, show me the code" -- Linus Torvalds
Website Link: http://web.iiit.ac.in/~rishi.mittal/ire/
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"Ink is better than the best memory." -- Chinese proverb