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TITLE: Googles PageRank Algorithm

GROUP MEMBERS: Anshu Malhotra (MT10001) & Mohona Ghosh (MT10011)

ABSTRACT

Google describes PageRank [1]:

PageRank reflects our view of the importance of web pages by considering more than

500 million variables and 2 billion terms. Pages that we believe are important pages

receive a higher PageRank and are more likely to appear at the top of the search

results.

PageRank is Googles Technology to innovate the way searches are conducted over the

Internet i.e. the over the Googles Search Engine which is the most popular search engine

today. Co-founded by Larry Page and named after him, it is a Googles technology to better

understand what the users want and give them back the best results for their search. The

software behind this search technology conducts a series of simultaneous calculations

requiring only a fraction of a second. On the other hand, most of the Traditional search

engines rely heavily on how often a word appears on a web page. Google Search uses more

than 200 signals, including their patented PageRank algorithm, to examine the entire linkstructure of the web and determine which pages are most important. Then hypertext-matching

analysis is conducted to determine which pages are relevant to the specific search being

conducted. By combining overall importance and query-specific relevance, Google is able to

put the most relevant and reliable results first [1].

World Wide Web is the huge repository of information which has over millions of web pages

linked to each other. Searching for the desired information in this huge store is a task in itself

which Google has optimised to great levels. A ranking algorithm can help the user to select

the best resources from this sea of Web Resources [2].

As a part of Advanced Algorithms course project, we plan to study the PageRank Algorithm

which is primarily based on Link Analysis and Web Graphs. This will involve studying about

Incremental Algorithms to compute PageRank for an evolving Web Graphs and to analyse

the computation costs related to it. We will learn about the structural properties of a Web

8/10/2019 Pagerank Anshu Mohona ProjectAbstract

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Graphs which will help us study the behaviour of algorithms in a typical web search. We plan

to study the following research papers as a part of our Advanced Algorithms term paper &

presentation:-

1. The Web as a graph: measurement, models and methods, Jon M. Kleinberg, Ravi

Kumar, Prabhakar Raghavan, Sridhar Rajagopalan, Andrew S. Tomkins.

2.

Incremental Page Rank Computation on Evolving Graphs, Prasanna Desikan, Nishith

Pathak, Jaideep Srivastava, Vipin Kumar

Reference Papers:

1. The Anatomy of a Large-Scale Hypertextual Web Search Engine, Sergey Brin and

Lawrence Page.

2. PageRank Uncovered, Written and theorised by Chris Ridings and Mike Shishigin,

Edited by Jill Whalen and Technical Editing by Yuri Baranov.

3. Dell Zhang and Yisheng Dong, An efficient algorithm to rank Web resources.

BIBLIOGRAPHY

1. http://www.google.com/corporate/tech.html

2. Dell Zhang and Yisheng Dong, An efficient algorithm to rank Web resources.