• Journal of the Institute of Electronics Engineers of Korea CI
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• v.46 no.5
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• pp.14-28
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• 2009

The PageRank algorithm is an important component for ranking Web pages in Google and other search engines. While many improvements for the original PageRank algorithm have been proposed, it is unclear which variations (and their combinations) provide the "best" ranked results. In this paper, we evaluate the ranking quality of the well-known variations of the original PageRank algorithm and their combinations. In order to do this, we first classify the variations into link-based approaches, which exploit the link structure of the Web, and knowledge-based approaches, which exploit the semantics of the Web. We then propose algorithms that combine the ranking algorithms in these two approaches and implement both the variations and their combinations. For our evaluation, we perform extensive experiments using a real data set of one million Web pages. Through the experiments, we find the algorithms that provide the best ranked results from either the variations or their combinations.

• Journal of KIISE:Computing Practices and Letters
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• v.11 no.3
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• pp.268-274
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• 2005

Since introduction of the PageRank technique, it is known that it ranks web pages effectively In spite of its usefulness, we found a computational drawback, which we call norm-leak, that PageRank values become smaller than they should be in some cases. We present an improved PageRank algorithm that computes the PageRank values of the web pages correctly as well as its efficient implementation. Experimental results, in which over 67 million real web pages are used, are also presented.

• The KIPS Transactions:PartD
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• v.9D no.5
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• pp.745-754
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• 2002

The Google search site (http://www.google.com), which was introduced in 1998, implemented the PageRank algorithm for the first time. PageRank is a ranking method based on the link structure of the Web pages. Even though PageRank has been implemented and being used in various commercial search engines, implementation details did not get documented well, primarily due to business reasons. Implementation techniques introduced in [4,8] are not sufficient to produce PageRank values of Web pages. This paper explains the techniques[4,8], and suggests major data structure and four implementation techniques in order to apply the PageRank algorithm. The paper helps understand the methods of applying PageRank algorithm by means of showing a real system that produces PageRank values of Web pages.

• Journal of The Korean Association of Information Education
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• v.22 no.4
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• pp.409-417
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• 2018

There are unplugged computer science activities for elementary school students to learn the concept of the Internet. However, these activities are not enough to teach the concept of the Web because they focus on teaching how the Internet works. Since the Web is the core technology of the Third Industrial Revolution, it needs to be understood as a basic common sense. In this paper, we developed an unplugged activity to understand the PageRank algorithm which is closely related to the web. The experimental results show that our unplugged activities behave similarly to the PageRank algorithm.

• Proceedings of the Korean Information Science Society Conference
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• 2003.10a
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• pp.169-171
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• 2003

웹에서의 검색 방법에는 크게 Text-Based 기법과 Link-Based 기법이 있다. 본 논문은 그 중에서 Link-Based 기법의 하나인 PageRank 알고리즘에 대해 연구 하고자 한다. 이 PageRank 알고리즘은 각 페이지의 중요성을 수치로 계산하는 방법이다. 하지만 이 알고리즘에서는 페이지에서 페이지로 링크를 따라갈 확률의 값을 일정하게 주어서 모든 페이지의 값을 획일적으로 계산하였기 때문에 각 페이지의 검색 효율성에 문제가 있다고 판단하여, 이를 해결하고자 본 논문은 페이지사이의 유사도를 측정하여 유사도에 따라 링크를 따라가는 확률 값인 Damping factor값을 다르게 부여하여 검색의 효율성을 높였다. 이를 위하여 두 가지 방법의 실험을 통하여 구현, 증명하였다.

• Proceedings of the Korean Society of Computer Information Conference
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• 2010.07a
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• pp.75-78
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• 2010

웹 검색에는 기존의 정보검색(Information Retrieval) 시스템에서와 다르게 문서 간 하이퍼링크 정보를 바탕으로 각 웹 문서의 고유 중요도를 추정하는 방식이 자주 이용된다. 링크 분석에 기반한 알고리즘 중 PageRank 알고리즘은 구글의 웹 검색 서비스에 적용된 것으로 알려져 있다. 이런 PageRank 알고리즘에 따라 중요도를 계산하는 경우 색인된 웹 문서수가 증가함에 따라 계산에 필요한 CPU 자원의 사용도 함께 증가하며, 문서 수가 수 억 페이지에 달하면 하나의 서버에서는 계산을 수행할 수 없다는 문제가 있다. 본 논문에서는 이런 문제점을 해소하기 위해 여러 대의 서버를 PageRank 계산 용 클러스터로 사용할 수 있는 방법을 제시한다. 제시된 방법은 고속의 LAN을 이용하여 여러 대의 서버를 연결하고 반복적인 행렬 계산을 병렬로 수행할 수 있어 계산 시간을 단축시킬 수 있다. 이런 서버 클러스터 구현을 위해 멀티 쓰레딩 프로그램이 작성되었으며, PageRank 계산에 사용되는 행렬 데이터를 적은 양의 메모리만으로 표현 가능하도록 하였다.

• Proceedings of the Korean Information Science Society Conference
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• 2005.07b
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• pp.1-3
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• 2005

최근 웹 검색 분야에서는 검색 질을 높이기 위한 기법들이 많이 연구되어 왔으며, 대표적인 연구로는 제한 검색, focused crawling, 웹 클러스터링 등이 있다. 그러나 제한 검색은 검색 범위를 의미적으로 관련된 사이트들로 제한할 수 없으며, focused crawling은 질의 시점에 클러스터링하기 때문에 질의 처리 시간이 오래 걸리고, 웹 클러스터링은 많은 웹 페이지들을 대상으로 클러스터링하기 위한 오버헤드가 크다. 본 논문에서는 검색 범위를 특정 커뮤니티로 제한하여 검색 하는 커뮤니티 제한 검색과 커뮤니티를 구하는 방법으로 cluster crawler를 제안하여 이러한 문제점을 해결한다. 또한, 커뮤니티를 이용하여 PageRank를 2단계로 계산하는 방법을 제안한다. 제안된 방법은 첫 번째 과정에서 커뮤니티 단위로 지역적으로 PageRank를 계산한 후, 두 번째 과정에서 이를 바탕으로 전역적으로 PageRank론 계산한다. 제안된 방법은 Wang에 의해 제안된 방법에 비해 PageRank 근사치의 오차를 $59\%$ 정도로 줄일 수 있다.

• Journal of Digital Contents Society
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• v.19 no.1
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• pp.157-163
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• 2018

People who use SNS publish their diverse ideas on SNS every day. The data posted on the SNS contains many people's thoughts and opinions. In particular, popular keywords served on Twitter compile the number of frequently appearing words in user posts and rank them. However, this method is sensitive to unnecessary data simply by listing duplicate words. The proposed method determines the ranking based on the topic of the word using the relationship diagram between words, so that the influence of unnecessary data is less and the main word can be stably extracted. For the performance comparison in terms of the descending keyword rank and the ratios of meaningless keywords among high rank 20 keywords, we make a comparison between the proposed scheme which is based on morphological analysis and PageRank, and the existing scheme which is based on the number of appearances. As a result, the proposed scheme and the existing scheme have included 55% and 70% of meaningless keywords among high rank 20 keywords, respectively, where the proposed scheme is improved about 15% compared with the existing scheme.

• Journal of the Korean BIBLIA Society for library and Information Science
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• v.22 no.4
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• pp.361-379
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• 2011

This paper aims to propose the most appropriate method for calculating the journal PageRank in a domestic citation database. Korean journals show relatively high journal self-citation ratios and have many outgoing citations to external journals which are not included in the domestic citation database. Because the PageRank algorithm requires recursive calculation to converge, those two characteristics of domestic citation databases must be accounted for in order to measure the citation impact of Korean journals. Therefore, two PageRank calculation methods and four formulas for self-citation adjustment have been examined and tested for KSCD journals. The results of the correlation analysis and regression analysis show that the SCImago Journal Rank formula with the cr2 type self-citation adjustment method seems to be a more appropriate way to measure the relative impact of domestic journals in the Korean Science Citation Database.

• The Korean Journal of Applied Statistics
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• v.29 no.4
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• pp.613-625
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• 2016

The development of technology has changed the world drastically. Patent data analysis helps to understand modern technology trends and predict prospective future technology. In this paper, we analyze the patent citation network using the USPTO data between 1985 and 2012 to identify technology trends. We use network centrality measures that include a PageRank algorithm to find core technologies and identify groups of technology with similar properties with statistical network models.