A unified framework for Web link analysis
Zheng Chen, Li Tao, Jidong Wang, Liu Wenyin, Wei‐Ying Ma
Abstract
Zheng Chen, Li Tao, Jidong Wang, Liu Wenyin, Wei‐Ying Ma
Abstract
Web link analysis has been proved to significantly enhance the precision of Web searching in practice. Among existing approaches, Kleinberg's (1998) HITS and Google's PageRank are the two most representative algorithms that employ explicit hyperlink structure among Web pages to conduct link analysis, and DirectHit represents the other extreme that takes the user's access frequency as an implicit link to the Web page for assessing its importance. We propose a novel link analysis algorithm which puts both explicit and implicit link structures under a unified framework, and show that HITS and DirectHit are essentially two extreme instances of our proposed method. One important advantage of our method is its ability to analyze not only the hyperlinks between Web pages but also the interactions between users and the Web at the same time. The importance of Web pages and users can reinforce each other to improve Web link analysis. Compared with traditional HITS and DirectHit algorithms, our method further improves the search precision by 11.8% and 25.3%.
OpenAlex reports 22 citations for this work. Citation counts describe recorded attention and do not establish research quality.
A contribution statement is not available in the OpenAlex record.
Method details are not available in the OpenAlex metadata.
Findings are not separately available in the OpenAlex metadata.
Limitations are not available in the OpenAlex metadata.
Application details are not available in the OpenAlex metadata.
Web link analysis has been proved to significantly enhance the precision of Web searching in practice. Among existing approaches, Kleinberg's (1998) HITS and Google's PageRank are the two most representative algorithms that employ explicit hyperlink structure among Web pages to conduct link analysis, and DirectHit represents the other extreme that takes the user's access frequency as an implicit link to the Web page for assessing its importance. We propose a novel link analysis algorithm which puts both explicit and implicit link structures under a unified framework, and show that HITS and DirectHit are essentially two extreme instances of our proposed method. One important advantage of our method is its ability to analyze not only the hyperlinks between Web pages but also the interactions between users and the Web at the same time. The importance of Web pages and users can reinforce each other to improve Web link analysis. Compared with traditional HITS and DirectHit algorithms, our method further improves the search precision by 11.8% and 25.3%.
Key concepts: Hyperlink, Link analysis, Computer science, HITS algorithm, Web page, Link (geometry), Information retrieval, Web mining