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Research Frontiers in Network Analysis and Graph Statistics

Statistical methods for analyzing complex networks and graph-structured data with applications to social and biological networks.

Mesoscale Organization in Complex Network Topologies
Information Diffusion Through Heterogeneous Network Layers
Statistical Inference of Hidden Network Structure
Dynamic Community Detection in Temporal Graphs
Sparse Graphical Models and High-Dimensional Network Recovery
Spectral Methods for Network Anomaly and Outlier Detection
Causal Structure Learning from Observational Network Data
Statistical Limits of Network Reconstruction Problems
Influence Maximization in Stochastic Network Environments
Adaptive Sampling Strategies for Large-Scale Graph Analysis

All Statistics PhD categories