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Proceedings of a Workshop on Statistics on Networks

Scott T. Weidman
Proceedings of a Workshop on Statistics on Networks by Scott T. Weidman PDF

Author : Scott T. Weidman
Publisher : National Academies Press
Page : 470 pages
File Size : 40,78 MB
Release : 2007-10-30
Category : Computers
ISBN : 0309101050

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A large number of biological, physical, and social systems contain complex networks. Knowledge about how these networks operate is critical for advancing a more general understanding of network behavior. To this end, each of these disciplines has created different kinds of statistical theory for inference on network data. To help stimulate further progress in the field of statistical inference on network data, the NRC sponsored a workshop that brought together researchers who are dealing with network data in different contexts. This book - which is available on CD only - contains the text of the 18 workshop presentations. The presentations focused on five major areas of research: network models, dynamic networks, data and measurement on networks, robustness and fragility of networks, and visualization and scalability of networks.

Massive Data Sets

National Research Council
Massive Data Sets by National Research Council PDF

Author : National Research Council
Publisher : National Academies Press
Page : 219 pages
File Size : 30,33 MB
Release : 1997-02-10
Category : Mathematics
ISBN : 0309056942

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Statistical Network Analysis: Models, Issues, and New Directions

Edoardo M. Airoldi
Statistical Network Analysis: Models, Issues, and New Directions by Edoardo M. Airoldi PDF

Author : Edoardo M. Airoldi
Publisher : Springer
Page : 204 pages
File Size : 22,63 MB
Release : 2008-04-12
Category : Computers
ISBN : 3540731334

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This book constitutes the thoroughly refereed post-proceedings of the International Workshop on Statistical Network Analysis: Models, Issues, and New Directions held in Pittsburgh, PA, USA in June 2006 as associated event of the 23rd International Conference on Machine Learning, ICML 2006. It covers probabilistic methods for network analysis, paying special attention to model design and computational issues of learning and inference.