JF IEEE Transactions on Knowledge & Data Engineering

YR 2013

VO 25

IS 11

SP 2658

TI Bias Correction in a Small Sample from Big Data

A1 Jianguo Lu,

A1 Dingding Li,

K1 Equations

K1 Estimation

K1 Sociology

K1 Statistics

K1 Mathematical model

K1 Twitter

K1 Information management

K1 size estimation

K1 Big data

K1 online social networks

K1 small sample

K1 bias

AB This paper discusses the bias problem when estimating the population size of big data such as online social networks (OSN) using uniform random sampling and simple random walk. Unlike the traditional estimation problem where the sample size is not very small relative to the data size, in big data, a small sample relative to the data size is already very large and costly to obtain. We point out that when small samples are used, there is a bias that is no longer negligible. This paper shows analytically that the relative bias can be approximated by the reciprocal of the number of collisions; thereby, a bias correction estimator is introduced. The result is further supported by both simulation studies and the real Twitter network that contains 41.7 million nodes.

PB IEEE Computer Society, [URL:http://www.computer.org]

SN 1041-4347

LA English

DO 10.1109/TKDE.2012.220

LK http://doi.ieeecomputersociety.org/10.1109/TKDE.2012.220