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Springer Social Networks Theory & Computing - Springer Lecture Notes

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Description

From Sociology to Computing in Social Networks explores the intersection of social network analysis and data mining, bridging theoretical foundations with practical applications in modern computing. This comprehensive volume from Springer's Lecture Notes in Social Networks series examines social network modelling, customisable infrastructure construction, and the identification of dynamic growth and evolution patterns that shape online communities. The work combines experimental and theoretical research to address industry needs and emerging trends, with particular emphasis on data mining techniques that unlock insights from large-scale social data. Designed for researchers, academics, and practitioners, this text provides the analytical frameworks and methodologies needed to understand social network behaviour and apply computational approaches to complex networking challenges.

Key Features

Covers social network modelling and theoretical foundations essential for understanding digital community structures and user behaviour patterns.

Examines customisable social network infrastructure construction techniques for building scalable and adaptable platforms.

Analyses dynamic growth and evolution patterns to identify how social networks develop and change over time.

Integrates data mining techniques with sociological theory to extract actionable insights from network data.

Published by Springer as part of the Lecture Notes in Social Networks series, providing academic rigour and credibility.

Product Specifications

Format
paperback
Domain
Amazon UK
Release Date
25 October 2016
Listed Since
19 December 2016

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