The random dot product graph (RDPG) is an independent-edge random graph that is
analytically tractable and, simultaneously, either encompasses or can successfully …

Brains contain networks of interconnected neurons and so knowing the network architecture
is essential for understanding brain function. We therefore mapped the synaptic-resolution …

Graphs arise naturally in many real-world applications including social networks,
recommender systems, ontologies, biology, and computational finance. Traditionally …

Recovering low-rank structures via eigenvector perturbation analysis is a common problem
in statistical machine learning, such as in factor analysis, community detection, ranking …

We analyze the performance of spectral clustering for community extraction in stochastic
block models. We show that, under mild conditions, spectral clustering applied to the …

There is a strong link between teamwork and patient safety. Emerging evidence supports the
efficacy of teamwork improvement interventions. However, the availability of reliable, valid …

Most methods proposed to uncover communities in complex networks rely on combinatorial
graph properties. Usually an edge-counting quality function, such as modularity, is optimized …

Spectral clustering is a fast and popular algorithm for finding clusters in networks. Recently,
Chaudhuri et al. and Amini et al. proposed variations on the algorithm that artificially inflate …

Community detection is a fundamental statistical problem in network data analysis. In this
paper, we present a polynomial time two-stage method that provably achieves optimal …

Significant efforts have gone into the development of statistical models for analyzing data in
the form of networks, such as social networks. Most existing work has focused on modeling …