Châtel B D L, Quax R, Peeters G, Corten R, Olde Rikkert M G M, Vasconcelos V V
Department of Geriatric Medicine, Radboud University Medical Center, Nijmegen, The Netherlands.
Computational Science Lab, Informatics Institute, University of Amsterdam, Amsterdam, The Netherlands.
Sci Rep. 2025 May 4;15(1):15576. doi: 10.1038/s41598-025-99057-x.
Loneliness, a pervasive mental health concern, is often misconstrued as an individual pathology, limiting our understanding of social effects via peer-to-peer interactions. This study investigates how homophily (similarity-based connectivity) and induction (interacting mental states) contribute to loneliness clustering. Using a computational model, we simulate social network interactions via established induction frameworks: emotional, behavioral, and cognitive contagion. We map these pathways to fundamental processes of simple contagion, complex contagion, and self-activation, explaining how ideas and behaviors spread. Results show that high homophily is necessary for loneliness clustering, and the model recovers empirical findings of network clustering (a positive correlation of individuals' mental states beyond direct neighbors) with extended "degrees of influence" across networks and setups. This universality of clustering across pathways renders the metric uninformative in screening causal mechanisms behind loneliness clustering. Fortunately, each inductive pathway displays distinct out-of-equilibrium dynamics, aiding in identifying real-world mechanisms. The study emphasizes the significant role of individuals' social contexts in loneliness and calls for a shift from static to dynamic measurements in loneliness research. This shift will enhance the relevance of future research on evolutionary patterns in real-world social network data, leading to a more robust understanding of the mechanisms of loneliness.
孤独，一种普遍存在的心理健康问题，常常被误解为个体的病理现象，这限制了我们通过 peer-to-peer 互动对社会影响的理解。本研究调查了同质性（基于相似性的连接性）和诱导（相互作用的心理状态）如何导致孤独的聚集。使用一个计算模型，我们通过既定的诱导框架模拟社会网络互动：情绪、行为和认知传染。我们将这些途径映射到简单传染、复杂传染和自我激活的基本过程，解释思想和行为是如何传播的。结果表明，高同质性是孤独聚集的必要条件，并且该模型通过跨网络和设置扩展的“影响程度”恢复了网络聚集的实证发现（个体心理状态超出直接邻居的正相关）。这种跨途径聚集的普遍性使得该指标在筛选孤独聚集背后的因果机制时缺乏信息。幸运的是，每条诱导途径都显示出独特的非平衡动态，有助于识别现实世界中的机制。该研究强调了个体社会背景在孤独中的重要作用，并呼吁在孤独研究中从静态测量转向动态测量。这种转变将增强未来对现实世界社会网络数据中进化模式研究的相关性，从而更深入地理解孤独的机制。
