III: Small: Towards Scalable and Efficient Graph Representation Learning With Modern Data — NSF Award to Louisiana State Universit
This project seeks to address a critical challenge in modern artificial intelligence (AI): efficiently analyzing large-scale graph data. Graphs are data structures used to represent interconnected information, such as social networks, molecular interactions, and recommendation systems. They are essential components in
| Award title | III: Small: Towards Scalable and Efficient Graph Representation Learning With Modern Data |
|---|---|
| Award ID | 2444247 |
| Awardee | Louisiana State University |
| City | BATON ROUGE |
| State | LA |
| Amount obligated | $569,210 |
| Principal investigator | Kisung Lee |
| Program | Info Integration & Informatics |
| Start date | 07/15/2025 |
| Abstract | This project seeks to address a critical challenge in modern artificial intelligence (AI): efficiently analyzing large-scale graph data. Graphs are data structures used to represent interconnected information, such as social networks, molecular interactions, and recommendation systems. They are essential components in a diverse array of applications across various industries, including healthcare, cybersecurity, and financial services. However, as graph data continues to grow in size and complex |
| Source | NSF Awards |
$799/mo
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