Generating molecular graphs using deep graph generative models is a challenging task that involves optimizing a given target within an enormous search space while adhering to chemical valence rules.

About Distribution-agnostic graph transformer architecture applied to landslide hazard modelling Readme Activity 1 star ...

To tackle the sampling challenge, we introduce Decoupled Graph Energy-based Model (DeGEM), which decomposes the learning process into two parts—a graph encoder that leverages topology information for ...

In 2008, for instance, the profit and loss distribution of Goldman Sachs looked more like an elongated “U” than the bell shape predicted by VaR. Normal distribution was no longer the norm.

Keywords: graph model, line loss calculation, hierarchical forward-backward sweep, bulk synchronous parallel computing model, distribution network Citation: Wang X, Chen W, Tian R, Ji Y and Zhu J ...

However, these models often struggle to mirror the GNN performance of the original graphs. In this work, we present SkyMap, a generative model for labeled attributed graphs with a fine-grained control ...

A range of modelling techniques have been suggested to implement integrated species distribution models (IDMs) including joint likelihood models, including one dataset as a covariate or informative ...