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Prediction over tabular data is an essential and fundamental problem in manyimportant downstream tasks. However, existing methods either take a datainstance of the table independently as input or do not fully utilize themulti-rows features and labels to directly change and enhance the target datarepresentations. In this paper, we propose to 1) construct a hypergraph fromrelevant data instance retrieval to model the cross-row and cross-columnpatterns of those instances, and 2) perform message Propagation to Enhance thetarget data instance representation for Tabular prediction tasks. Specifically,our specially-designed message propagation step benefits from 1) fusion oflabel and features during propagation, and 2) locality-aware high-order featureinteractions. Experiments on two important tabular data prediction tasksvalidate the superiority of the proposed PET model against other baselines.Additionally, we demonstrate the effectiveness of the model components and thefeature enhancement ability of PET via various ablation studies andvisualizations. The code is included in https://github.com/KounianhuaDu/PET.

Learning Enhanced Representations for Tabular Data via Neighborhood Propagation