menu
techminis

A naukri.com initiative

google-web-stories
Home

>

ML News

>

A Deep Pro...
source image

Arxiv

3d

read

140

img
dot

Image Credit: Arxiv

A Deep Probabilistic Framework for Continuous Time Dynamic Graph Generation

  • Recent advancements in graph representation learning have led to a focus on dynamic graphs.
  • There is a need for generative models that can handle continuously changing temporal graph data.
  • In this work, a new approach called DG-Gen is proposed to model interactions in dynamic graphs using joint probabilities.
  • DG-Gen outperforms traditional methods in generating high-fidelity graphs and improving link prediction tasks.

Read Full Article

like

8 Likes

For uninterrupted reading, download the app