• 응용통계연구
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• 제36권1호
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• pp.1-19
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• 2023

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.

• Communications for Statistical Applications and Methods
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• 제30권6호
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• pp.561-575
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• 2023

Brain network analysis has attracted the interest of neuroscience researchers in studying brain diseases. Magnetoencephalography (MEG) is especially proper for analyzing functional connectivity due to high temporal and spatial resolution. The application of graph theory for functional connectivity analysis has been studied widely, but research on network modeling for MEG still needs more. Temporal exponential random graph model (TERGM) considers temporal dependencies of networks. We performed the brain network analysis, including static/temporal network statistics, on two groups of epilepsy patients who removed the left (LT) or right (RT) part of the brain and healthy controls. We investigate network differences using Multiset canonical correlation analysis (MCCA) and TERGM between epilepsy patients and healthy controls (HC). The brain network of healthy controls had fewer temporal changes than patient groups. As a result of TERGM, on the simulation networks, LT and RT had less stable state than HC in the network connectivity structure. HC had a stable state of the brain network.