<ul data-eligibleForWebStory="true"><li>This study focuses on exploring the importance of pairwise interactions in constructing feature graphs for Graph Neural Networks (GNNs).</li><li>The research leverages existing GNN models and tools to analyze the relationship between feature graph structures and their effectiveness in modeling interactions.</li><li>Experiments on synthesized datasets reveal that edges connecting interacting features play a crucial role in enabling GNNs to effectively model feature interactions.</li><li>Including non-interaction edges in the feature graph can introduce noise and degrade model performance.</li><li>The study also introduces theoretical support for selecting sparse feature graphs based on the Minimum Description Length (MDL) principle.</li><li>Sparse feature graphs, retaining only necessary interaction edges, are shown to provide a more efficient and interpretable representation compared to complete graphs, in line with Occam's Razor.</li><li>The findings offer theoretical insights and practical guidelines for designing feature graphs to enhance the performance and interpretability of GNN models.</li></ul>

From Features to Graphs: Exploring Graph Structures and Pairwise Interactions via GNNs

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