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