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<p>This study examines a graph-based learning framework for modelling associations between work-from-home (WFH) furniture layouts and users’ affective responses in immersive virtual reality (VR). Forty-three participants evaluated two layout conditions—self-arranged and researcher-arranged—in a controlled 4.2 × 4.2&#xa0;m room environment, yielding 85 valid layout cases. Layouts were reconstructed in Unity and rated on six outcomes: perceived visual complexity, satisfaction, concentration, sense of control, inclination to use the workspace, and perceived performance. Each layout was represented as a spatial graph, with furniture and architectural elements encoded as nodes and proximity-based relationships encoded as edges. A GraphSAGE model with attention pooling jointly predicted the six outcomes under weighted MSE, achieving a mean absolute error of 0.684 and variance-weighted R² of approximately 0.659 in five-fold cross-validation. Compared with non-graph baseline models, the graph-based model showed lower prediction error in this dataset while also providing interpretable node- and edge-level importance patterns. GNNExplainer highlighted desk-related relationships, including desk–window, desk–door, and desk–storage configurations, as influential subgraphs. Rather than establishing a general theory of space–emotion interaction, the study demonstrates the methodological value of graph-based representation for evaluating how object-level spatial relationships in WFH layouts are associated with user experience.</p>

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Graph-based analysis of work-from-home layouts and affective responses in virtual reality

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年6月29日

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  • Sung-Bin Yoon,
  • So Yeon Park

摘要

This study examines a graph-based learning framework for modelling associations between work-from-home (WFH) furniture layouts and users’ affective responses in immersive virtual reality (VR). Forty-three participants evaluated two layout conditions—self-arranged and researcher-arranged—in a controlled 4.2 × 4.2 m room environment, yielding 85 valid layout cases. Layouts were reconstructed in Unity and rated on six outcomes: perceived visual complexity, satisfaction, concentration, sense of control, inclination to use the workspace, and perceived performance. Each layout was represented as a spatial graph, with furniture and architectural elements encoded as nodes and proximity-based relationships encoded as edges. A GraphSAGE model with attention pooling jointly predicted the six outcomes under weighted MSE, achieving a mean absolute error of 0.684 and variance-weighted R² of approximately 0.659 in five-fold cross-validation. Compared with non-graph baseline models, the graph-based model showed lower prediction error in this dataset while also providing interpretable node- and edge-level importance patterns. GNNExplainer highlighted desk-related relationships, including desk–window, desk–door, and desk–storage configurations, as influential subgraphs. Rather than establishing a general theory of space–emotion interaction, the study demonstrates the methodological value of graph-based representation for evaluating how object-level spatial relationships in WFH layouts are associated with user experience.