Sampling-guided Heterogeneous Graph Neural Network with Temporal Smoothing for Scalable Longitudinal Data Imputation

Zhaoyang Zhang, Ziqi Chen, Qiao Liu, Jinhan Xie, Hongtu Zhu

Keywords: Graph Neural Network, Missing data imputation, Longitudinal data, Representative learning

Abstract: In this paper, we propose a novel framework, the Sampling-guided Heterogeneous Graph Neural Network ($\text{S\small{HT-GNN}}$), to effectively tackle the challenge of missing data imputation in longitudinal studies. Unlike traditional methods, which often require extensive preprocessing to handle irregular or inconsistent missing data, our approach accommodates arbitrary missing data patterns while maintaining computational efficiency. $\text{S\small{HT-GNN}}$ models both observations and covariates as distinct node types, connecting observation nodes at successive time points through subject-specific longitudinal subnetworks, while covariate-observation interactions are represented by attributed edges within bipartite graphs. By leveraging subject-wise mini-batch sampling and a multi-layer temporal smoothing mechanism, $\text{S\small{HT-GNN}}$ efficiently scales to large datasets, while effectively learning node representations and imputing missing data. Extensive experiments on both synthetic and real-world datasets, including the Alzheimer's Disease Neuroimaging Initiative ($\text{A\small{DNI}}$) dataset, demonstrate that $\text{S\small{HT-GNN}}$ significantly outperforms existing imputation methods, even with high missing data rates (e.g., 80\%). The empirical results highlight $\text{S\small{HT-GNN}}$’s robust imputation capabilities and superior performance, particularly in the context of complex, large-scale longitudinal data.

Primary Area: learning on time series and dynamical systems

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Submission Number: 4113