Collaborative Research: III: Medium: Empowering Graph Neural Networks from a Data Perspect — NSF Award to Michigan State Universit
Graph Neural Networks (GNNs) are a powerful class of artificial intelligence models that help analyze complex relationships within data, from understanding how our brains function to predicting molecular interactions or identifying financial anomalies. While these models have shown remarkable promise, their widespread
| Award title | Collaborative Research: III: Medium: Empowering Graph Neural Networks from a Data Perspect |
|---|---|
| Award ID | 2504089 |
| Awardee | Michigan State University |
| City | EAST LANSING |
| State | MI |
| Amount obligated | $300,000 |
| Principal investigator | Hui Liu |
| Program | Info Integration & Informatics |
| Start date | 08/01/2025 |
| Abstract | Graph Neural Networks (GNNs) are a powerful class of artificial intelligence models that help analyze complex relationships within data, from understanding how our brains function to predicting molecular interactions or identifying financial anomalies. While these models have shown remarkable promise, their widespread application in the real world faces significant hurdles: they often struggle to process extremely large datasets, adapt to unseen data, and maintain reliability when faced with int |
| Source | NSF Awards |
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