Generating Web Graphs with Embedded Communities

Author(s):  
Vivek B. Tawde ◽  
Tim Oates ◽  
Eric Glover
Keyword(s):  
2015 ◽  
Vol 12 (1) ◽  
pp. 33-48 ◽  
Author(s):  
Balázs Kósa ◽  
Márton Balassi ◽  
Péter Englert ◽  
Attila Kiss

In our paper we compare two centrality measures of networks, betweenness and Linerank. Betweenness is widely used, however, its computation is expensive for large networks. Calculating Linerank remains manageable even for graphs of billion nodes, it was offered as a substitute of betweenness in [12]. To the best of our knowledge the relationship between these measures has never been seriously examined. We calculate the Pearson?s and Spearman?s correlation coefficients for both node and edge variants of these measures. For edges the correlation tends to be rather low. Our tests with the Girvan-Newman algorithm [16] also underline that edge betweenness cannot be substituted with edge Linerank. The results for the node variants are more promising. The correlation coefficients are close to 1. Notwithstanding, the practical application in which the robustness of social and web graphs is examined node betweenness still outperforms node Linerank. We also clarify how Linerank should be computed on undirected graphs.


2002 ◽  
Vol 33 (3) ◽  
pp. 296-296 ◽  
Author(s):  
Dennis A. Kaufman ◽  
Rebecca S. Kaufman
Keyword(s):  

Author(s):  
Zhenglu Yang ◽  
Jeffrey Xu Yu ◽  
Zheng Liu ◽  
Masaru Kitsuregawa
Keyword(s):  

2011 ◽  
pp. 1437-1461 ◽  
Author(s):  
Rui Lopes ◽  
Luís Carriço

Web Accessibility is a hot topic today. Striving for social inclusion has resulted in the requirement of providing accessible content to all users. However, since each user is unique, and the Web evolves in a decentralized way, little or none is known about the shape of the Web’s accessibility on its own at a large scale, as well as from the point-of-view of each user. In this chapter the authors present the Web Accessibility Knowledge Framework as the foundation for specifying the relevant information about the accessibility of a Web page. This framework leverages Semantic Web technologies, side by side with audience modeling and accessibility metrics, as a way to study the Web as an entity with unique accessibility properties dependent from each user’s point of view. Through this framework, the authors envision a set of queries that can help harnessing and inferring this kind of knowledge from Web graphs.


Author(s):  
Rui Lopes ◽  
Luís Carriço

Web Accessibility is a hot topic today. Striving for social inclusion has resulted in the requirement of providing accessible content to all users. However, since each user is unique, and the Web evolves in a decentralized way, little or none is known about the shape of the Web’s accessibility on its own at a large scale, as well as from the point-of-view of each user. In this chapter the authors present the Web Accessibility Knowledge Framework as the foundation for specifying the relevant information about the accessibility of a Web page. This framework leverages Semantic Web technologies, side by side with audience modeling and accessibility metrics, as a way to study the Web as an entity with unique accessibility properties dependent from each user’s point of view. Through this framework, the authors envision a set of queries that can help harnessing and inferring this kind of knowledge from Web graphs.


Sign in / Sign up

Export Citation Format

Share Document