Learning from incomplete heterogeneous tabular data is challenging because missingness often exhibits structural dependencies across samples and features rather than occurring independently. Viewing tabular data as a partially observed bipartite graph, missing entries correspond to removed edges and structured missingness manifests as sparsification patterns in the sample–feature interaction graph. To address this problem, we propose IVGAE, a variational graph framework that jointly models latent data representations and missingness mechanisms. IVGAE performs variational inference over the bipartite graph to capture structural dependencies among heterogeneous features while modeling missingness as a graph-level generative process. A dual-decoder architecture simultaneously reconstructs feature values and infers missingness patterns under MCAR, MAR, and MNAR regimes. To effectively encode heterogeneous attributes, we introduce a Transformer-based feature embedding module that preserves categorical semantics without relying on high-dimensional one-hot representations. Experiments on 16 real-world datasets demonstrate that IVGAE consistently improves reconstruction accuracy and downstream prediction performance compared with strong imputation baselines across diverse missing regimes. The codes are available at: https://github.com/echoid/IVGAE .
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