Discovering small-world in association link networks for web-based learning

Authors:
Shunxiang Zhang;Xiangfeng Luo;Junyu Xuan;Xue Chen;Weimin Xu
Affiliations:
School of Computer Engineering and Science, Shanghai University, Shanghai, China;School of Computer Engineering and Science, Shanghai University, Shanghai, China;School of Computer Engineering and Science, Shanghai University, Shanghai, China;School of Computer Engineering and Science, Shanghai University, Shanghai, China;School of Computer Engineering and Science, Shanghai University, Shanghai, China
Venue:
MTDL '11 Proceedings of the third international ACM workshop on Multimedia technologies for distance learning
Year:
2011

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Discovering small-world in association link networks for association learning

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Abstract

Association Link Network (ALN) is a kind of Semantic Link Network built by mining the association relations among Web resources for effectively supporting Web intelligent application such as Web-based learning, and knowledge acquisition. This paper explores the Small-World properties of ALN to provide theoretical support for Web-based learning. First, a filtering algorithm of ALN is proposed to generate the filtered status of ALN by adjusting the filtering parameter. Secondly, the Small-World properties of ALN at the filtered status are calculated and analyzed by regression analysis to observe the changing trend of Small-World properties. After that, comparison of the Small-World properties between ALN and random graph shows that ALN reveals prominent Small-World characteristic. The discovery of Small-World characteristic of ALN can provide theoretical support for Web-based learning.