Collaborative Research: III: Small: An Information-Theoretic Framework for Explainable and — NSF Award to Florida International Un
Graphs are powerful tools for representing relationships in complex systems, from social networks to weather monitoring stations. Graph Neural Networks (GNNs) have emerged as effective methods for analyzing these interconnected systems, but their "black box" nature poses significant challenges in critical applications
| Award title | Collaborative Research: III: Small: An Information-Theoretic Framework for Explainable and |
|---|---|
| Award ID | 2529283 |
| Awardee | Florida International University |
| City | MIAMI |
| State | FL |
| Amount obligated | $332,925 |
| Principal investigator | Mo Sha |
| Program | Info Integration & Informatics |
| Start date | 10/01/2025 |
| Abstract | Graphs are powerful tools for representing relationships in complex systems, from social networks to weather monitoring stations. Graph Neural Networks (GNNs) have emerged as effective methods for analyzing these interconnected systems, but their "black box" nature poses significant challenges in critical applications such as environmental monitoring, healthcare, and finance. This project develops a comprehensive framework for making GNN predictions explainable and trustworthy. The research addr |
| Source | NSF Awards |
$799/mo
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