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TERGM과 SAOM 비교 : 학생 네트워크 데이터의 통계적 분석
Comparison of TERGM and SAOM : Statistical analysis of student network data
Korean J Appl Stat 2023;36(1):1-19
Published online February 28, 2023
© 2023 The Korean Statistical Society.

한유진a, 김재희1, a
Yujin Hana, Jaehee Kim1, a

a덕성여자대학교 정보통계학과

aDepartment of Statistics, Duksung Women’s University
1Department of Statistics, Duksung Women’s University, 419 Samyang-ro, 144 Gil 33, Dobong-Gu, Seoul 01369, Korea. E-mail: jaehee@duksung.ac.kr
Received October 8, 2022; Revised November 27, 2022; Accepted November 27, 2022.
Abstract
본 연구는 학생 간의 연결에 어떤 속성이 유효한지 종단 네트워크 분석을 통해 알아보고자 하였으며, 종단 네트워크 모형인 TERGM (temporal exponential random graph model)과 SAOM (stochastic actor-oriented model) 통계적 모형을 사용하고 결과를 비교하였다. TERGM 모형은 네트워크 전체의 연결 형성을 바탕으로, SAOM 모형은 특정 행위자가 형성하는 주변 네트워크를 대상으로 연구 결과를 해석하였다. TERGM 모형은 시간 항을 통해 이전 시점의 영향을 표현하였으며, SAOM 모형은 비율 함수로 행위자의 기회에 의해 진화하는 네트워크를 구현해 시간적 종속성을 고려하였다.
The purpose of this study was to find out what attributes are valid for the edge between students through longitudinal network analysis, and the results of TERGM (temporal exponential random graph model) and SAOM (stochastic actor-oriented model) statistical models were compared. The TERGM model interprets the research results based on the edge formation of the entire network, and the SAOM model interprets the research results on the surrounding networks formed by specific actors. The TERGM model expressed the influence of a previous time through a time term, and the SAOM model considered temporal dependence by implementing a network that evolves by an actor’s opportunity as a ratio function.
주요어 : 종단 네트워크, TERGM, SAOM, 학생, 웨어러블
Keywords : temporal network, TERGM, SAOM, student, wearable
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