arXiv:2510.03096v3 Announce Type: replace Abstract: We propose an adaptive node feature selection approach for graph neural networks (GNNs) that identifies and removes unnecessary features during training. The ability to measure how features contribute to model output is key for interpreting decisions and reducing dimensionality by eliminating unhelpful variables. However, graph-structured data introduces complex dependencies that may be unsuited to classical feature importance metrics. Inspired by this, we present a data-, model-, and task-agnostic method that determines relevant features dur
The increasing complexity and scale of GNNs necessitate more efficient and interpretable model training, driving innovation in feature selection techniques.
Adaptive feature selection can significantly improve GNN performance, reduce computational costs, and enhance the interpretability of AI decisions in critical applications.
GNNs can now be potentially trained with greater efficiency and precision by automatically discarding irrelevant features, making their deployment in demanding scenarios more feasible.
- · AI researchers and developers
- · Companies deploying GNNs
- · Sectors reliant on graph data analysis
- · Inefficient GNN architectures
- · Brute-force feature engineering methods
More robust and less computationally intensive GNN models become available, accelerating AI development.
Improved interpretability of GNNs could lead to their adoption in highly regulated fields where decision transparency is paramount.
This could contribute to the development of more generalized and less data-hungry AI models, impacting the broader AI landscape.
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Read at arXiv cs.LG