Enhancement in Weighted Page Rank Algorithm for Ranking Web Pages

Sowmiya.A Gayathri.A

Damodharan.P

Department Of Computer Science and Engineering

sowmyaa.ashokan@gmail.com

Abstract

To retrieve required information from World Wide

Web, search engines perform number of tasks based

on their respective architecture. Web structure

mining, is one such task and one of three categories

of web mining for data, and is a tool used to identify

the relationship between web pages linked by

information or direct link connection. This structure

data is discoverable by the provision of web structure

schema through database techniques for web pages.

A fast and efficient page ranking methods for web

crawling and retrieval remains as a challenging issue, most of the ranking algorithm are either link or

content oriented, which does not consider the user

usage behaviour. In this paper, a page ranking

mechanism called optimized Weighted Page Rank

algorithm being developed for search engines, which

works on the basis of weighted page rank algorithm

and takes number of visits of inbound links of web

pages into account. This algorithm tends to be very

useful in reterving more relevant information

according to user’s query. So, this concept is very

useful to display most valuable pages on the top of

the result list on the basis of user browsing behavior,

which reduce the search space to a large size.

Keywords- in link, out link ,weighted page rank

1. Introduction WWW continues to grow at an astounding

rate resulting in increase of complexity of tasks such

as web site design web server design and of simply

navigating through a web site. The WWW is huge,

widely distributed, global information service centre

for Information services, Hyper-link information and

Access and usage information. This tends to be very

difficult in discerning and Providing relevant

information to the users. Only a small portion of the

information on the Web is truly relevant or useful. it

is true that a particular person is generally interested

in only a tiny portion of the Web, while the rest of the

Web contains information that is uninteresting to the

user and may swamp desired search results. One of

the most important challenging issues in any web

search engine is finding high quality web search.

Web mining is the integration of information

gathered by traditional data mining methodologies

and techniques with information gathered over the

World Wide Web. It looks for patterns in data

through content mining, structure mining, and usage

mining. Web Content Web Content Mining is used to

examine data collected by search engines, Web

spiders and focuses on the discovery or retrieval of

the useful information from the Web contents. Web

Usage Mining is used to examine data related to a

particular user's browser as well as data gathered by

forms the user may have submitted during Web

communications and predicts the user's behaviors.

Web Structure Mining is used to examine data related

to the structure of a particular Web site emphasizes to

the discovery of how to model the underlying link

structures of the Web. It also identify the relationship

between Web pages linked by information or direct

link connection. It discovers the link structure of

hyper link at the inter document level. This type of

mining can be performed at document level as intra

page or at hyperlink level as inter page. It basically

consider the numbers of inlinks (links to a page) and

of outlinks (links from a page). In this paper

Optimized page rank is proposed which is relies on

Web Structure Mining , uses interconnection between

web pages to give weight to pages.

2. Related Works

The first well-known algorithm for ranking

web pages is page rank proposed by Lawrence Page

and Sergey Brin. Page Rank is a way to rank Web

pages taking into account hyper-link structure of the

Sowmiya A et al , Int.J.Computer Technology & Applications,Vol 5 (1),140-143

IJCTA | Jan-Feb 2014 Available online@www.ijcta.com

140

ISSN:2229-6093

Web. Page Rank provides a efficient and simple

method to find out ranking of web pages exploiting

hyperlink structure of web.Using Page Rank, it is

capable to order search results so that more

significant and central Web pages are given

preference. The intuition behind Page Rank is that it

uses information which is external to the Web pages

themselves their back links, which provide a kind of

peer review. Furthermore, back links from important

pages are more momentous than back links from

average pages. Therefore the importance of any web

page can be judged by looking at the pages that link

to it. In other words, Pages are ranked high if number

of back link is high. Page Rank of a document is

always determined recursively by the Page Rank of

other documents. The major issues in the Page Rank algorithm is in

the actual web, some links in a web page may be

more important than are the others. Rank is equally

distributed to its outgoing links .

J. Kleinberg have identified a form of

equilibrium among WWW sources on a common

topic, Hyperlink-Induced Topic Search (HITS) is a

link analysis algorithm that rates Web pages. It was a

precursor to Page Rank. In the HITS algorithm, the

first step is to retrieve the most relevant pages to the

search query. This set is called the root set and can be

obtained by taking the top n pages returned by a text-

based search algorithm. These pages are then

expanded to a larger root sets as Base set by adding

any pages thar are linked to or from any page. Hits

has two kinds of useful pages as Authority page that

contains a lot of information about the query topic

and Hub page that contains a large number of links to

pages containing information. Some pages, the most prominent sources of primary content, are the

authorities on the topic, other pages, equally intrinsic

to the Kleinberg Hubs, Authorities, and

Communities. It is completely natural and many good

hubs on the Web are being created by relatively

anonymous individuals, and the main authorities on a

topic are often in competition with one another, either

explicitly or implicitly.

The issues here are its difficult to discern between

hubs and authority, not efficient in real time and

Topic drift problem occurs .

Ali Mohammad Zareh Bidoki et,al proposed

a technique as Distance Ranking based on

reinforcement learning as to avoid the problem of

“richer gets richer” problem. In this, distance

between pages are considered as punishment.

Distance is defined as the number of “average clicks” between two pages .The page with low distance will

have a higher rank. The issue is that it is good only

for small number no of iterations.

Wenpu Xing et.al proposed weighted page

rank which overcomes the problem of page rank. The

Weighted Page Rank algorithm(WPR) is an

extension of standard Page Rank algorithm. WPR

takes into account the importance of both the inlinks

and the outlinks of the pages and distributes rank

scores based on the popularity of the pages, which is

able to identify a larger digit of relevant pages to a

given query compared to Standard Page Rank. Each

outlink page gets a value proportional to its

popularity. According to Xing the more popular web

pages are the more linkages that other web pages tend

to have to them or are linked to by them. It returns

the large number of relevant pages to the user based

on query rather than the standard page rank

algorithm. Inlink of a page is calculated as given in

equation (1),

(1)

Where,

represent the number of in-links of page u

represent the number of in-links of page p

respectively.

denotes the reference page list of page v.

(v,u) is the weight of link(v, u) .

(v,u) is calculated based on the number of out

links of page u and the number of out links of all

reference pages of page v.

Similarly outlink of a page is calculated as given in

equation (2),

(2)

Where,

represent the number of out links of page u.

represent the number of out links of page page

p.

denotes the reference page list of page v.

And finally,the weighted page rank is calculated by

the formula by given in equation (3),

Sowmiya A et al , Int.J.Computer Technology & Applications,Vol 5 (1),140-143

IJCTA | Jan-Feb 2014 Available online@www.ijcta.com

141

ISSN:2229-6093

Drawbacks: There is a less determination in relevancy of pages to

a given query

The algorithm relies mainly on the number connected

in links and out links.

It does not consider the user usage behaviour.

A page irrelevant to the query still receives a high

priority because of its many inlinks and outlinks.

3. Proposed System

The proposed system which is Optimized

Weighted Page Rank (OWPR) will enable the search

engine to present the best related pages to the user in

response to the queries. However the current ranking

algorithm are either link or content oriented and does

not take into account the user usage trends. The

original WPR takes both the inlink and outlink and

distribute the rank score based on the popularity .

Optimized WPR gives higher rank value to the outgoing link which is most visited by user and

neglect the popularity of outgoing link i.e W out

(v,u).

It make use of both web structure mining i.e.uses

interconnection between web pages to give weight to

pages and web usage mining i.e mining for user

navigation pattern. OWPR takes the number of visits

of inbound links of web pages is taken into

consideration. The rank of web page using this

algorithm can be calculated as given in equation (4),

Where,

U represents the web pages.

B(u) is the set of pages that point to u.

d denotes Dampening factor.

OWPR(u) is rank scores of page u.

(v) is rank scores of page v.

Lu is the number of visits of link which is pointing

page u from v.

TL(v) denotes total number of visits of all links

present on v.

4. Result

OWPR calculates Page Rank value or

importance of web pages based on the visits of

incoming links on a page as well as the popularity of

inlinks of a web page. This method uses link

structure of pages, the popularity of inlinks and their

browsing information, the top returned pages in the

result list is supposed to be highly relevant to the user

information needs. A link with high probability of

visit contributes more towards the rank of its out

linked pages. The rank value of any page by original

Weighted Page Rank method will be same either it is

seen by user or not, because it is totally dependent

upon link structure of Web graph and popularity of

inlinks and outinks. While the ordering of pages

using OWPR is more target-oriented.

Performance Analysis

The proposed algorithm is finding

more relevant information according to

user’s query. So, the concept is very useful

to display most valuable pages on the top of

the result list on the basis of user browsing

behaviour, which reduce the search space to

a large scale.

Fig 1 Comparision of WRR and OWPR.

Sowmiya A et al , Int.J.Computer Technology & Applications,Vol 5 (1),140-143

IJCTA | Jan-Feb 2014 Available online@www.ijcta.com

142

ISSN:2229-6093

5. Conclusion

Due to the oceans of information available finding

the high quality web pages that are relevant to the

user’s query are difficult. The proposed Optimized

WPR makes use of the user usage behavior and that

the more relevant results are retrieved first. Thus

the relevant information are retrieved to the user

more quickly and efficiently.

6. References [1] Gyanendra Kumar, Neelam Duahn, and Sharma A. K., “Page Ranking Based on Number of Visits of Web

Pages”, International Conference on Computer &

Communication Technology (ICCCT)-2011, 978-1-

4577-1385-9. [2] Rekha Jain, Dr.G.N.Purohit., “Page Ranking

Algorithms for WebMining” ,International Journal of

Computer application,Vol 13, Jan 2011.

[3] T. Ravi Kumar, and Singh Ashutosh kumar., “Web Structure Mining Exploring Hyperlinks and Algorithms

for Information Retrieval”, American Journal of applied

sciences, 7 (6) 840-845 2010. [4] N. Duhan, A. K. Sharma and Bhatia K. K., “Page

Ranking Algorithms: A Survey”, Proceedings of the

IEEE International Conference on Advance Computing,

2009, 978-1-4244-1888-6 [5] Ali Mohammad Zareh Bidoki, Nasser Yazdani,

“DistanceRank: An intelligent ranking algorithm for web

pages”, Information Processing and management,

Elsevier, June 2007 [6] Wenpu Xing and Ghorbani Ali, “Weighted PageRank

Algorithm”, Proceedings of the Second Annual

Conference on Communication Networks and Services

Research (CNSR ’04), IEEE, 2004. [7] J.Wang, Z. Chen, L. Tao, W. Ma, and W. Liu.

Ranking user’s relevance to a topic through link analysis

on web logs. WIDM, pages 49–54, 2002.

[8] J. Hou and Y. Zhang., “Effectively Finding Relevant Web Pages from Linkage Information”, IEEE

Transactions on Knowledge and Data Engineering, Vol.

15, No. 4, 2003.

[9] R. Kosala, and H. Blockeel, “Web Mining Research: A Survey”, SIGKDD Explorations, Newsletter of the

ACM Special Interest Group on Knowledge Discovery

and Data Mining Vol. 2, No. 1 pp 1-15, 2000.

[10] J. M. Kleinberg. Authoritative sources in a hyperlinked environment. Journal of the ACM,

46(5):604–632, September1999.

Sowmiya A et al , Int.J.Computer Technology & Applications,Vol 5 (1),140-143

IJCTA | Jan-Feb 2014 Available online@www.ijcta.com

143

ISSN:2229-6093