Discovering small-world in association link networks for association learning

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• 作者：Shunxiang Zhang (1) (2)
  Xiangfeng Luo (1)
  Junyu Xuan (1)
  Xue Chen (1)
  Weimin Xu (1)
  
• 关键词：semantic network ; association link network ; small ; world ; association learning
• 刊名：World Wide Web
• 出版年：2014
• 出版时间：March 2014
• 年：2014
• 卷：17
• 期：2
• 页码：229-254
• 全文大小：3,803 KB
• 作者单位：Shunxiang Zhang (1) (2)
  Xiangfeng Luo (1)
  Junyu Xuan (1)
  Xue Chen (1)
  Weimin Xu (1)
  
  1. School of Computer Engineering and Science & High Performance Computing Center, Shanghai University, Shanghai, 200072, China
  2. School of Computer Science and Engineering, Anhui University of Science & Technology, Huainan, 232001, China
  
• ISSN：1573-1413

文摘

Association Link Network (ALN) is a kind of Semantic Link Network built by mining the association relations among multimedia Web resources for effectively supporting Web intelligent application such as Web-based learning, and semantic search. This paper explores the Small-World properties of ALN to provide theoretical support for association learning (i.e., a simple idea of “learning from Web resources-. First, a filtering algorithm of ALN is proposed to generate the filtered status of ALN, aiming to observe the Small-World properties of ALN at given network size and filtering parameter. Comparison of the Small-World properties between ALN and random graph shows that ALN reveals prominent Small-World characteristic. Then, we investigate the evolution of Small-World properties over time at several incremental network sizes. The average path length of ALN scales with the network size, while clustering coefficient of ALN is independent of the network size. And we find that ALN has smaller average path length and higher clustering coefficient than WWW at the same network size and network average degree. After that, based on the Small-World characteristic of ALN, we present an Association Learning Model (ALM), which can efficiently provide association learning of Web resources in breadth or depth for learners.