MoFlow: an invertible flow model for generating molecular graphs
-
Updated
Mar 14, 2023 - Python
MoFlow: an invertible flow model for generating molecular graphs
A Data-Driven Graph Generative Model for Temporal Interaction Networks
DYnamic MOtif-NoDes (DYMOND) is a dynamic network generative model based on temporal motifs and node behavior.
SCOTT: Synthesizing Curvature Operations and Topological Tools
Supporting code for the NeurIPS 2024 paper 'Diffusion Twigs with Loop Guidance for Conditional Graph Generation'.
DYnamic Attributed Node rolEs (DYANE) is an attributed dynamic-network generative model based on temporal motifs and attributed node behavior.
Add a description, image, and links to the graph-generative-model topic page so that developers can more easily learn about it.
To associate your repository with the graph-generative-model topic, visit your repo's landing page and select "manage topics."