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 …
L Gulikers, M Lelarge, L Massoulié - Advances in Applied Probability, 2017 - cambridge.org
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 …
S Spreizer, A Aertsen, A Kumar - PLoS computational biology, 2019 - journals.plos.org
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 …
J Shore, B Lubin - Social Networks, 2015 - Elsevier
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 …