Statistical Learning and Inference for Network Data with Positive and Negative Edges — NSF Award to Carnegie Mellon University (PA
Networks, representing relationships or interactions between subjects in complex systems, are ubiquitous across diverse engineering and scientific disciplines. However, real-world relationships often go beyond simple presence or absence, which poses challenges and necessitates the development of advanced methods. This
| Award title | Statistical Learning and Inference for Network Data with Positive and Negative Edges |
|---|---|
| Award ID | 2412853 |
| Awardee | Carnegie Mellon University |
| City | PITTSBURGH |
| State | PA |
| Amount obligated | $166,597 |
| Principal investigator | Weijing Tang |
| Program | STATISTICS |
| Start date | 07/01/2024 |
| Abstract | Networks, representing relationships or interactions between subjects in complex systems, are ubiquitous across diverse engineering and scientific disciplines. However, real-world relationships often go beyond simple presence or absence, which poses challenges and necessitates the development of advanced methods. This project focuses on an important class of heterogeneous networks -- “signed networks”, where relationships can be positive (for example, friendship, alliance, and mutualism) or nega |
| Source | NSF Awards |
$799/mo
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