AI 中文总结
研究在线社交网络中感知到的社会支持与网络距离的关系，通过链接调查数据与行为日志，用随机森林推断支持，发现其随网络距离呈幂律衰减，模拟表明异质衰减率会产生重尾聚合衰减，强调考虑网络位置异质性的重要性。
AI 中文摘要
感知到的社会支持可以缓冲压力，但在不同图形距离的在线社交网络中，其如何关联仍不清楚。本文表明，在一个大型虚拟形象通信应用程序中，推断出的感知在线社交支持随网络距离衰减，在观察范围内，幂衰减模型比单指数模型能更好地描述这种衰减形式。我们将来自Pigg Party的两波调查数据与行为日志相链接，训练随机森林以推断活跃用户的感知支持，并对跳距为k的用户，将第二波分数对第一波分数进行回归，同时调整基线支持和协变量。调整后的关联在各跳中持续存在，与观察范围内的幂律衰减一致。基于个体的模拟表明，特定源的异质指数衰减率可产生重尾聚合衰减。这些结果表明，在描述在线社区中心理社会状态间的距离依赖关联时，应考虑网络位置异质性。
英文摘要
Perceived social support can buffer stress, but how it is associated across online social networks at different graph distances remains unclear. Here we show that inferred perceived online social support in a large avatar communication application decays with network distance in a form better described, over the observed range, by a power-decay model than by a single exponential. We linked two-wave survey data from Pigg Party with behavioral logs, trained a random forest to infer perceived support for active users, and regressed Wave 2 scores on Wave 1 scores for users at hop distance $k$, adjusting for baseline support and covariates. Adjusted associations persisted across hops, consistent with power-law-like decay over the observed range. Individual-based simulations indicated that heterogeneous source-specific exponential decay rates can generate heavy-tailed aggregate decay. These results suggest that network position heterogeneity should be considered when characterizing distance-dependent associations among psychosocial states in online communities.
