Sampling Online Social Networks: An Experimental Study of Twitter - Inria - Institut national de recherche en sciences et technologies du numérique Access content directly
Poster Communications Year : 2014

Sampling Online Social Networks: An Experimental Study of Twitter

Abstract

Online social networks (OSNs) are an important source of information for scientists in different fields such as computer science, sociology, economics, etc. However, it is hard to study OSNs as they are very large. For instance, Facebook has 1.28 billion active users in March 2014 and Twitter claims 255 million active users in April 2014. Also, com-panies take measures to prevent crawls of their OSNs and refrain from sharing their data with the research community. For these reasons, we argue that sampling techniques will be the best technique to study OSNs in the future. In this work, we take an experimental approach to study the characteristics of well-known sampling techniques on a full social graph of Twitter crawled in 2012 [2]. Our contri-bution is to evaluate the behavior of these techniques on a real directed graph by considering two sampling scenarios: (a) obtaining most popular users (b) obtaining an unbiased sample of users, and to find the most suitable sampling tech-niques for each scenario.
Fichier principal
Vignette du fichier
gabielkov_sampling.pdf (235.4 Ko) Télécharger le fichier
figures/sampling_ratio_poster.pdf (14.08 Ko) Télécharger le fichier
figures/top1000_poster.pdf (82.3 Ko) Télécharger le fichier
Origin : Files produced by the author(s)
Origin : Files produced by the author(s)
Origin : Files produced by the author(s)
Loading...

Dates and versions

hal-01096980 , version 1 (18-12-2014)

Identifiers

Cite

Maksym Gabielkov, Ashwin Rao, Arnaud Legout. Sampling Online Social Networks: An Experimental Study of Twitter. ACM SIGCOMM 2014, Dec 2014, Chicago, IL, United States. ⟨10.1145/2619239.2631452⟩. ⟨hal-01096980⟩

Collections

INRIA INRIA2
206 View
469 Download

Altmetric

Share

Gmail Facebook X LinkedIn More