Geometric intersection graphs form an intriguing class of structures in which vertices represent geometric objects – such as line segments, discs, or curves – and an edge is established between two ...

Vertices arrive sequentially in space and are joined to existing vertices at random according to a preferential rule combining degree and spatial proximity. We investigate phase transitions in the ...

A geometric sequence is a sequence of numbers where each number is obtained by multiplying the previous number by a constant value. Geometric sequences are non-linear. That is, when they’re graphed ...

Abstract: ‘Double edge swaps’ transform one graph into another while preserving the graph's degree sequence, and have thus been used in a number of popular Markov chain Monte Carlo (MCMC) sampling ...

Abstract: In this work, we study the generalization capabilities of graph neural networks (GNNs) over geometric graphs sampled from manifolds. Specifically, we focus on graphs constructed from ...

PyG (PyTorch Geometric) is a library built upon PyTorch to easily write and train Graph Neural Networks (GNNs) for a wide range of applications related to structured data. Whether you are a machine ...

Given knowledge of the amino acid sequence and of some version of the 3D structure of two monomers that are expected to form a complex, the authors investigate whether it is possible to accurately ...

Information visualization is a classic topic that has become crucial in our information-heavy digital age – from slick infographics to detailed interactive cartographic maps, they help us visually ...