Comparison of graph structure is a ubiquitous task in data analysis and machine learning,
with diverse applications in fields such as neuroscience, cyber security, social network …

Real‐world networks usually have community structure, that is, nodes are grouped into
densely connected communities. Community detection is one of the most popular and best …

We consider community detection in degree-corrected stochastic block models. We propose
a spectral clustering algorithm based on a suitably normalized adjacency matrix. We show …

Spontaneous activity is a fundamental characteristic of the developing nervous system.
Intriguingly, it often takes the form of multiple structured assemblies of neurons. Such …

Unraveling the interplay between connectivity and spatio-temporal dynamics in neuronal
networks is a key step to advance our understanding of neuronal information processing …

Spatio-temporal sequences of neuronal activity are observed in many brain regions in a
variety of tasks and are thought to form the basis of meaningful behavior. However …

We derive the limiting distribution for the outlier eigenvalues of the adjacency matrix for
random graphs with independent edges whose edge probability matrices have low-rank …

We introduce a new statistic,'spectral goodness of fit'(SGOF) to measure how well a network
model explains the structure of the pattern of ties in an observed network. SGOF provides a …

A myriad of approaches have been proposed to characterize the mesoscale structure of
networks most often as a partition based on patterns variously called communities, blocks, or …

We consider the canonical ensemble of N-vertex Erdős-Rényi (ER) random topological
graphs with quenched vertex degree, and with fugacity μ for each closed triple of bonds. We …