This $350,000 National Science Foundation project grant supports statistical modeling research for complex networks at the University of Michigan from September 2022 through August 2025. Funded through the NSF's Mathematical and Physical Sciences program (CFDA 47.049), the award aims to develop new statistical methodologies and theory to incorporate higher-order structures into network modeling. Specifically, the investigators will study leveraging subgraphs and other higher-order structures...
The National Science Foundation Division of Social and Economic Science awarded Trustees of Boston University a $449,985 project grant under the Social, Behavioral, and Economic Sciences program (CFDA 47.075). The grant period is from August 1, 2021 to July 31, 2024 and will support the development of new coevolving latent space network with attractors models to study social dynamics. Specifically, the awardee will develop modeling and statistical inference methodologies for this new class of...
This National Science Foundation project grant awarded $100,000 to Arizona State University to fund research into opinion formation and graph dynamics on social media platforms from September 1, 2022 to August 31, 2025. The award aims to develop mathematical models of the interplay between social networks and opinion dynamics to better understand the rise of polarization and echo chambers in online discourse. A subaward of $25,000 was provided to Artis Research & Risk Modeling Corporation to...
This National Science Foundation (NSF) Social, Behavioral, and Economic Sciences (CFDA 47.075) Project Grant award of $300,000 to Florida State University (FSU) supports the development of a new class of latent variable models for network data. The research aims to create models that can capture essential characteristics observed in network data from various disciplines, including the social and life sciences. Key activities include integrating and extending existing approaches to modeling...
This $400,000 Project Grant award from the National Science Foundation's (NSF) Social, Behavioral, and Economic Sciences (CFDA 47.075) program will fund the development of new kernel-based techniques for social network analysis. The research will advance methodology for exploratory, comparative, and semiparametric network analysis, building on proven approaches and creating new tools to meet the needs of practitioners. Key objectives include progressing network regression methods using kernel...
This National Science Foundation (NSF) Social, Behavioral, and Economic Sciences (CFDA 47.075) Project Grant provides $329,937 to Yale University to develop a computational framework for understanding how people infer relationships and social structures through observing patterns of interpersonal interactions. The 3-year project (7/1/2025 - 6/30/2028) aims to 1) develop a theory and computational model of how humans learn about underlying group dynamics, and 2) conduct experiments to test and...
The National Science Foundation awarded a $156,866 Project Grant to the University of Chicago under the Social, Behavioral, and Economic Sciences program. The grant will fund research developing new statistical and econometric methods for analyzing social network data. Specifically, the project aims to build methodology for incorporating high-dimensional and incomplete network data into empirical models. Researchers will create a nonparametric regression framework for networks and investigate...
The University of Pittsburgh was awarded a $150,000 Project Grant from the National Science Foundation Division of Mathematical Sciences under the Mathematical and Physical Sciences federal grant program (CFDA 47.049) to develop new statistical inference tools and theories for analyzing data with network dependency. Specifically, the award will support modeling and inference research for data exhibiting complex interpersonal dependency characterized by networks, with a focus on developing...
This three-year project grant from the National Science Foundation's Mathematical and Physical Sciences program, totaling $359,940, will support the development of new statistical models and algorithms for analyzing large, spatially-dependent data sets collected from complex domains with irregular boundaries. Specifically, the awardee, Texas A&M University, will introduce a class of nonstationary models that can flexibly characterize potentially heterogeneous spatial dependence while...
This National Science Foundation project grant of $300,000 will fund research at the University of California, Los Angeles to develop scalable Bayesian dimension reduction methods for analyzing massive, dynamic network data related to global viral epidemics. Specifically, the award will support extending Bayesian multidimensional scaling to enable analysis of network data involving millions of observations. Theoretical and methodological work will develop a sparse coupling model and efficient...