网络链接数据的预测模型
Prediction models for network-linked data
摘要：
预测算法通常假设训练数据是独立的样本，但在许多现代应用中，样本来自通过网络连接的个体。例如，在青少年冒险行为的健康研究中，受试者的社会网络信息通常是可用的，并通过网络凝聚力发挥重要作用，经验观察到的朋友行为相似的现象。在预测模型中考虑内聚性应该使我们能够提高它们的性能。在这里，我们提出了一个基于网络的惩罚单个节点的影响，以鼓励链接节点的预测之间的相似性，并表明，将其纳入预测导致改善传统的模型在理论上和经验上，当网络凝聚力存在。惩罚可以与许多基于损失的预测方法一起使用，例如回归、广义线性模型和Cox比例风险模型。详细讨论了AddHealth研究中基于人口统计协变量和友谊网络的青少年娱乐活动和大麻使用水平的预测应用，并表明我们考虑友谊的方法可以显着提高行为预测，同时提供可解释的协变量效应估计。
英文摘要：
Prediction algorithms typically assume the training data are independent samples, but in many modern applications samples come from individuals connected by a network. For example, in adolescent health studies of risk-taking behaviors, information on the subjects' social network is often available and plays an important role through network cohesion, the empirically observed phenomenon of friends behaving similarly. Taking cohesion into account in prediction models should allow us to improve their performance. Here we propose a network-based penalty on individual node effects to encourage similarity between predictions for linked nodes, and show that incorporating it into prediction leads to improvement over traditional models both theoretically and empirically when network cohesion is present. The penalty can be used with many loss-based prediction methods, such as regression, generalized linear models, and Cox's proportional hazard model. Applications to predicting levels of recreational activity and marijuana usage among teenagers from the AddHealth study based on both demographic covariates and friendship networks are discussed in detail and show that our approach to taking friendships into account can significantly improve predictions of behavior while providing interpretable estimates of covariate effects.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
