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DiscoWeb: Applying link analysis to web search (1999)
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| Venue: | In Poster Proceedings of the Eighth International World Wide Web Conference |
| Citations: | 20 - 4 self |
BibTeX
@INPROCEEDINGS{Davison99discoweb:applying,
author = {Brian D. Davison and Apostolos Gerasoulis and Konstantinos Kleisouris and Yingfang Lu and Hyun-ju Seo and Wei Wang and Baohua Wu},
title = {DiscoWeb: Applying link analysis to web search},
booktitle = {In Poster Proceedings of the Eighth International World Wide Web Conference},
year = {1999},
pages = {148--149}
}
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Abstract
How often does the search engine of your choice produce results that are less than satisfying, generating endless links to irrelevant pages even though those pages may contain the query keywords? How often are you given pages that tell you things you already know? While the search engines and related tools continue to make improvements in their information retrieval algorithms, for the most part they continue to ignore an essential part of the web – the links. We have found that link analysis can have significant contributions to web page retrieval from search engines, to web community discovery, and to the measurement of web page influence. It can help to rank results and find high-quality index/hub/link pages that contain links to the best sites on the topic of interest. Our work is based on research from IBM’s CLEVER project [7, 4, 6], Stanford’s Google [3], and the Web Archaeology research [2, 1] at Compaq’s Systems Research Center. These research teams have demonstrated some of the contributions that link analysis can make in the web. In our work, we have attempted to generalize and improve upon these approaches. Just as in citation analysis of published works, the most influential documents on the web will have many other documents recommending (pointing to) them. This idea underlies all link analysis efforts, from the straightforward technique of counting the number of incoming edges to a page, to the deeper eigenvector analysis used in our work and in those projects mentioned above. It turns out that the identification of “high-quality ” web pages reduces to a sparse eigenvalue of the adjacency matrix
Keyphrases
applying link analysis search engine analysis effort high-quality index hub link page choice produce result ibm clever project web archaeology research link analysis eigenvector analysis system research center web page influence influential document significant contribution straightforward technique information retrieval algorithm sparse eigenvalue related tool citation analysis many document page retrieval endless link adjacency matrix high-quality web page essential part community discovery research team query keywords
