Bootstrap Method for Social Network Analysis

• 발행기관: 한국자료분석학회
• 저자명: 홍금재(GeumJae Hong) 강창완(Changwan Kang) 김규곤(Kyu Kon Kim)
• 간행물 정보: 『Journal of The Korean Data Analysis Society (JKDAS)』Vol.17 No.4, 1739~1745쪽, 전체 7쪽
• 주제분류: 자연과학 > 통계학
• 파일형태: PDF
• 발행일자: 2015.08.30

영문 초록

In social network analysis, we use various measures for networks, but we don’t give these statistics with these standard errors. Generally It is useful to have an indication of how precise measures are, particularly when making inference for the network statistics. In order to calculate these standard errors, we use the bootstrap method. The basic idea of the bootstrap is that the observed data are treated as a population in itself, and that random samples of size N are drawn with replacement from the observed data. In this paper we introduce a bootstrap method for social network analysis by ties resampling and give bootstrap confidence intervals for centrality measures and density of real data, Dongeui friendship data. As a result, we found that the network density of Dongeui friendship data is 0.021, 95% bootstrap confidence intervals are 0.0174 and 0.0255. In the Dongeui friendship network, we performed a test of hypothesis that two major actors have same centrality measures, and we knew that these centrality scores are not significantly different from each other.

목차

1. Introduction

2. Bootstrap standard errors for network statistics

3. A practical example

4. Conclusions

References

키워드

social networks centrality closeness betweenness density bootstrap