This $400,000 federal Project Grant award from the National Science Foundation's (NSF) Social, Behavioral, and Economic Sciences (CFDA 47.075) program will support research to develop new techniques for analyzing social network data. The project aims to advance kernel-based machine learning methods for exploring complex relationships and patterns in social networks. Key goals include enhancing network regression modeling, creating novel families of network similarity kernels, and extending widely used exponential random graph modeling approaches. The research will produce new analytical tools and methodologies to address substantive challenges in areas like public safety, disaster response, and business decision-making. Broader impacts include developing educational materials, incorporating new techniques into coursework, and providing freely available software for government, industry, and the public. This 3-year award to the University of California, Irvine is focused on delivering innovative solutions for network analysis that combine advancements in machine learning with rigorous statistical theory.
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