具有节点异质性和同质性的动态网络
Dynamic Networks with Node Heterogeneity and Homophily
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中文总结 AI 辅助
本文针对动态网络建模节点异质性与链接同质性的问题，提出新框架与归一化平方损失方法，经理论分析及模拟、真实网络数据验证，可实现高维场景下参数的高效稳定估计，为动态网络演化分析及未来网络预测提供支撑。
中文摘要 AI 辅助
本文旨在为动态网络建模节点异质性和链接同质性，所提框架为网络随时间演化的方式提供了新见解，也为未来网络预测提供了带有统计保证的更复杂工具。新模型考虑了与观测特征和潜在特征相关的链接同质性，而节点异质性以及观测和潜在同质性效应的联合建模，因模型中存在大量混杂参数，给统计推断带来重大挑战。为克服该问题，我们提出一种新的归一化平方损失，为高维场景下参数的高效稳定估计铺平了道路。我们对该估计方法进行了严格的理论分析，并通过大量模拟和真实网络数据的实例验证了其有效性。
英文摘要
The goal of this paper is to model node heterogeneity and link homophily for dynamic networks. The proposed framework brings new insights on how networks evolve over time. It also provides more sophisticated tools for the prediction of future networks with statistical guarantees. The new model accounts for the link homophily associated with both observed traits and latent traits. The joint modeling of node heterogeneity and both observed and latent homophily effects also poses the significant challenge in statistical inference, resulted from the large number of confounding parameters in the model. To overcome this, we propose a novel normalized squared loss, paving the way for efficient and stable estimation of parameters in a high-dimensional setting. We provide a rigorous theoretical analysis of the estimation method, and demonstrate its effectiveness through extensive simulations and the illustration with some real-world network data.
