Social Network Analysis (SNA) is an established discipline for the study of groups of individuals with applications in several areas like economics, information science, organizational studies and psychology. In the last fifteen years the exponential growth of on-line Social Network Sites like Facebook, QQ and Twitter has provided a new challenging application context for SNA methods. However, with respect to traditional SNA application domains these systems are characterized by very large volumes of data, and this has recently led to the development of parallel network analysis algorithms and libraries. In this chapter we provide an overview of the state of the art in the field of large scale social network analysis; in particular we focus on parallel algorithms and libraries for the computation of network centrality metrics.

Large-scale Social Network Analysis

LAMBERTINI, MATTIA;MAGNANI, MATTEO;MARZOLLA, MORENO;MONTESI, DANILO;
2014

Abstract

Social Network Analysis (SNA) is an established discipline for the study of groups of individuals with applications in several areas like economics, information science, organizational studies and psychology. In the last fifteen years the exponential growth of on-line Social Network Sites like Facebook, QQ and Twitter has provided a new challenging application context for SNA methods. However, with respect to traditional SNA application domains these systems are characterized by very large volumes of data, and this has recently led to the development of parallel network analysis algorithms and libraries. In this chapter we provide an overview of the state of the art in the field of large scale social network analysis; in particular we focus on parallel algorithms and libraries for the computation of network centrality metrics.
2014
Large-Scale Data Analytics
155
187
M. Lambertini; M. Magnani; M. Marzolla; D. Montesi; C. Paolino
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11585/215658
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