Generating Web Graphs with Embedded Communities

2004 ◽  
pp. 80-91 ◽  
Author(s):  
Vivek B. Tawde ◽  
Tim Oates ◽  
Eric Glover
Keyword(s):  
Web Graphs

scholarly journals Building Knowledge Maps of Web Graphs

2018 ◽  
Author(s):  
Valeria Fionda ◽  
Giuseppe Pirro ◽  
Claudio Gutierrez
Keyword(s):  
Knowledge Maps ◽  
Web Graphs

scholarly journals Betweenness versus Linerank

2015 ◽  
Vol 12 (1) ◽  
pp. 33-48 ◽  
Author(s):  
Balázs Kósa ◽  
Márton Balassi ◽  
Péter Englert ◽  
Attila Kiss
Keyword(s):  
Correlation Coefficients ◽  
Centrality Measures ◽  
Practical Application ◽  
Undirected Graphs ◽  
Large Networks ◽  
Web Graphs ◽  
Edge Betweenness ◽  
The Relationship

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.

Interactive Web Graphs for Economic Principles

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

Dominating Sets in Web Graphs

2004 ◽  
pp. 31-43 ◽  
Author(s):  
Colin Cooper ◽  
Ralf Klasing ◽  
Michele Zito
Keyword(s):  
Dominating Sets ◽  
Web Graphs

Fires on the Web: Towards Efficient Exploring Historical Web Graphs

2010 ◽  
pp. 612-626 ◽  
Author(s):  
Zhenglu Yang ◽  
Jeffrey Xu Yu ◽  
Zheng Liu ◽  
Masaru Kitsuregawa
Keyword(s):  
Web Graphs ◽  
The Web

Querying Web Accessibility Knowledge from Web Graphs

2011 ◽  
pp. 1437-1461 ◽  
Author(s):  
Rui Lopes ◽  
Luís Carriço
Keyword(s):  
Social Inclusion ◽  
Large Scale ◽  
Web Accessibility ◽  
Relevant Information ◽  
Point Of View ◽  
Semantic Web Technologies ◽  
Web Technologies ◽  
Knowledge Framework ◽  
Web Graphs ◽  
The Web

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.

Querying Web Accessibility Knowledge from Web Graphs

2010 ◽  
pp. 88-112 ◽  
Author(s):  
Rui Lopes ◽  
Luís Carriço
Keyword(s):  
Social Inclusion ◽  
Large Scale ◽  
Web Accessibility ◽  
Relevant Information ◽  
Point Of View ◽  
Semantic Web Technologies ◽  
Web Technologies ◽  
Knowledge Framework ◽  
Web Graphs ◽  
The Web

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.

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