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Toward a next generation of network models for the Web
Abstract It is generally thought that the World WidIt is generally thought that the World Wide Web belongs to the class of complex networks that is scale-free: the distribution of the number of links that nodes have follows a power law ('rich-get-richer' effect). This phenomenon is explained by a combination of theoretical-computational and empirical analysis based on stochastic network models. However, current network models embody a number of assumptions and idealizations that are not valid for the Web. Better and richer network models are needed, in association with a much more refined and in-depth empirical data gathering and analysis. In particular, the understanding of the dynamics leaves much to desire. In this paper we present a dynamic network model that avoids a number of unrealistic idealizations commonly introduced. We show how properties such as average degree and power laws are the outcome of dynamic network parameters. Exemplified by a Wikipedia case study, we show how these dynamic parameters might be empirically measured directly. We falsify several widely held ideas about the emergence of power laws: (i) that they are related to growing networks; (ii) that they are related to (linear) preferential attachment; (iii) that they may hold strictly. Power laws do not have the status of a first principle in networks: if they hold, they are just conditional and approximate empirical regularities.al and approximate empirical regularities.
Abstractsub It is generally thought that the World WidIt is generally thought that the World Wide Web belongs to the class of complex networks that is scale-free: the distribution of the number of links that nodes have follows a power law ('rich-get-richer' effect). This phenomenon is explained by a combination of theoretical-computational and empirical analysis based on stochastic network models. However, current network models embody a number of assumptions and idealizations that are not valid for the Web. Better and richer network models are needed, in association with a much more refined and in-depth empirical data gathering and analysis. In particular, the understanding of the dynamics leaves much to desire. In this paper we present a dynamic network model that avoids a number of unrealistic idealizations commonly introduced. We show how properties such as average degree and power laws are the outcome of dynamic network parameters. Exemplified by a Wikipedia case study, we show how these dynamic parameters might be empirically measured directly. We falsify several widely held ideas about the emergence of power laws: (i) that they are related to growing networks; (ii) that they are related to (linear) preferential attachment; (iii) that they may hold strictly. Power laws do not have the status of a first principle in networks: if they hold, they are just conditional and approximate empirical regularities.al and approximate empirical regularities.
Bibtextype inproceedings  +
Doi 10.1145/2464464.2464517  +
Has author Akkermans H. + , Bakhshi R. +
Has extra keyword Degree distributions + , Dynamic network models + , Hyperlink networks + , Nonlinear preferential attachment + , Power law + , World Wide Web + , Hypertext systems +
Has keyword Degree distributions + , Dynamic network models + , Nonlinear preferential attachment + , Power law + , Wikipedia hyperlink network +
Isbn 9781450318891  +
Language English +
Number of citations by publication 0  +
Number of references by publication 0  +
Pages 1–10  +
Published in Proceedings of the 3rd Annual ACM Web Science Conference, WebSci 2013 +
Title Toward a next generation of network models for the Web +
Type conference paper  +
Year 2013 +
Creation dateThis property is a special property in this wiki. 7 November 2014 12:30:11  +
Categories Publications without license parameter  + , Publications without remote mirror parameter  + , Publications without archive mirror parameter  + , Publications without paywall mirror parameter  + , Conference papers  + , Publications without references parameter  + , Publications  +
Modification dateThis property is a special property in this wiki. 7 November 2014 12:30:11  +
DateThis property is a special property in this wiki. 2013  +
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