There is a consensus that substantial heterogeneity underlies the neurobiology of autism
spectrum disorder (ASD). As such, it has become increasingly clear that a dissection of …

The development of models and methodology for the analysis of data from multiple
heterogeneous networks is of importance both in statistical network theory and across a …

The data science of networks is a rapidly develo** field with myriad applications. In
neuroscience, the brain is commonly modeled as a connectome, a network of nodes …

Spectral embedding is a procedure which can be used to obtain vector representations of
the nodes of a graph. This paper proposes a generalisation of the latent position network …

Using brain atlases to localize regions of interest is a requirement for making
neuroscientifically valid statistical inferences. These atlases, represented in volumetric or …

In this article we propose a lightning fast graph embedding method called one-hot graph
encoder embedding. It has a linear computational complexity and the capacity to process …

Given a point set P⊆ X of size n in a metric space (X, dist) of doubling dimension d and two
parameters k∈ ℕ and z∈ ℕ, the k-center problem with z outliers asks to return a set …

Multireference (MR) diagnostics are common tools for identifying strongly correlated
electronic structure that makes single-reference (SR) methods (eg, density functional theory …

Cascading failures can easily occur and cause a major blackout in power systems when a
critical devices breaks down. It is an essential problem to evaluate the importance of …

Increasing the reproducibility of neuroimaging measurement addresses a central
impediment to the advancement of human neuroscience and its clinical applications. Recent …