Project Grant 2311160

This federal Project Grant award from the National Science Foundation (NSF) under the Computer and Information Science and Engineering (CISE, CFDA 47.070) program provides $300,000 to Northeastern University to develop a novel approach called "Graphides" for analyzing and predicting phenomena using sparse graph data. The project aims to establish a rigorous theoretical framework for studying the limits and properties of sparse random graph models, with applications in areas such as neuroscience, machine learning, and telecommunication networks. The primary methods involve using the theory of projective limits to identify categories of sparse random graph models that have "graphides" as their limits. By applying this methodology to models like random hyperbolic graphs, the project seeks to demonstrate the existence of graphides and how they can enable more accurate predictions from sparse relational data. This 3-year grant, awarded on December 1, 2023, supports Northeastern University's interdisciplinary research to advance the state-of-the-art in analyzing and harnessing sparse network data.