Carter Butts
University of California-Irvine, CA·Mathematics & Statistics
Social systems are webs of relationships: who talks to whom, who trusts whom, who lives near whom. Analyzing these networks statistically is hard because each tie depends on others.
- Fields
Mathematics & Statistics
Social Science
Computer Science
- Topics
social network analysis
statistical network analysis
kernel methods
- Grants
- 1 known grant, through Mar 2028
What they work on
- Advance network regression methods using kernel learning, which relies on functions that score the similarity between complex objects, to measure how attributes and geography shape social ties.
- Develop new families of kernels for comparing whole social networks, adapting graph kernels first used for molecular structures in chemometrics.
- Build kernel-based extensions of exponential family random graph models (ERGMs), and apply them to substantive problems such as disaster response and communication about public safety threats.
More about the research
Researchers want to know how traits such as personal similarity, geographic closeness or shared social settings shape these ties. This project develops new statistical methods for this question by combining machine learning with rigorous statistical theory.
Why it matters
Better tools for analyzing social networks could improve decisions in business and government. They could inform applied problems such as communicating public safety threats and responding to disasters. Freely available software would let government agencies, industry, researchers and the public apply these methods to their own network data.
Known grants
From NIH and NSF research grants. Labs often have other funding we can't see.
Closest labs
Jonathan Stewart
Florida State University, FL·Mathematics & Statistics
Develop a new class of generalized latent position models for network data that drop the usual conditional independence assumption, so that dependence among connections, not just node positions, shapes the modeled network.
Yinqiu He
University of Wisconsin-Madison, WI·Mathematics & Statistics
Develop methods with theoretical guarantees for estimating low-dimensional network embeddings and quantifying their uncertainty under general network models with relaxed assumptions.
Zack Almquist
University of Washington, WA·Social Science·Early-career
Estimate the size and demographics of hard-to-reach homeless populations online, using generalized network scale-up methods and network sampling with people who access the internet via smartphones, free Wi-Fi and library computers.
The school
University of California-Irvine: 46 labs on PI Pages, $645M a year on research, 524 research doctorates a year.
Reach out
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