Blair D. Sullivan

University of Utah · United States

structural graph theoryefficient algorithmsgenerative network modelsdata-driven sciencenetwork structureparameterized algorithmsanomaly detectiongraph data

简介

Blair D. Sullivan leads a research group combining expertise in structural graph theory, efficient algorithms, and generative network models with tools from geometry and statistics to address fundamental questions in data-driven science. Current research focuses on improving understanding of intermediate-scale network structure and developing efficient parameterized algorithms for comparison, anomaly detection, sampling, and feature approximation in real-world graph data.

代表成果

Edge-Colored Clustering in Hypergraphs: Beyond Minimizing Unsatisfied Edges
Cluster Editing with Vertex Splitting
A Space-Efficient Algebraic Approach to Robotic Motion Planning

数据校验于 9/6/2026数据来源

学生评价

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