Paper Info
Title
Discovering small-world in association link networks for association learning
Abstract
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 of ALN scales with the network size, while of ALN is independent of the network size. And we find that ALN has smaller and higher 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.
Year
DOI
Venue
2014
10.1007/s11280-012-0171-7
World Wide Web
Keywords
Field
DocType
semantic network,association link network,small-world,association learning
Network size,Data mining,Random graph,Computer science,Theoretical computer science,Artificial intelligence,Clustering coefficient,Web resource,Average path length,Semantic search,Filter (signal processing),Semantic network,Machine learning
Journal
Volume
Issue
ISSN
17
2
1386-145X
Citations 
PageRank 
References 
11
0.55
24
Authors
5
Name
Order
Citations
PageRank
Shunxiang Zhang112518.93
Xiangfeng Luo21251124.38
Junyu Xuan3597.20
Xue Chen41588.11
Weimin Xu5617.98