Temporal point processes (TPP) are probabilistic generative models for continuous-time
event sequences. Neural TPPs combine the fundamental ideas from point process literature …

Semi-supervised node classification in graphs is a fundamental problem in graph mining,
and the recently proposed graph neural networks (GNNs) have achieved unparalleled results …

We propose NetGAN-the first implicit generative model for graphs able to mimic real-world
networks. We pose the problem of graph generation as learning the distribution of biased …

Tensors are multidimensional arrays of numerical values and therefore generalize matrices
to multiple dimensions. While tensors first emerged in the psychometrics community in the …

Temporal point processes are the dominant paradigm for modeling sequences of events
happening at irregular intervals. The standard way of learning in such models is by estimating …

The ongoing pandemic of SARS-CoV-2 presents novel challenges and opportunities for the
use of phylogenetics to understand and control its spread. Here, we analyze the emergence …

Theoretical description and numerical simulation of an evaporating sessile drop are developed.
We jointly take into account the hydrodynamics of an evaporating sessile drop, effects of …

Community detection is a fundamental problem in machine learning. While deep learning
has shown great promise in many graphrelated tasks, developing neural models for …

It was shown earlier by IM Lifshitz and two of us that the evolution of the relativistic
cosmological models towards the singularity undergoes spontaneous stochastization. (1) In the …

Human populations outside of Africa have experienced at least two bouts of introgression
from archaic humans, from Neanderthals and Denisovans. In Papuans there is prior evidence …