SOTAVerified

Graph Generation

Graph Generation is an important research area with significant applications in drug and material designs.

Source: Graph Deconvolutional Generation

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 91–100 of 712 papers

TitleStatusHype
HiGen: Hierarchical Graph Generative NetworksCode1
Graph Generation with K^2-treesCode1
PiVe: Prompting with Iterative Verification Improving Graph-based Generative Capability of LLMsCode1
Learning Joint 2D & 3D Diffusion Models for Complete Molecule GenerationCode1
MolHF: A Hierarchical Normalizing Flow for Molecular Graph GenerationCode1
Efficient and Degree-Guided Graph Generation via Discrete Diffusion ModelingCode1
Unbiased Scene Graph Generation in VideosCode1
SPAN: Learning Similarity between Scene Graphs and Images with TransformersCode1
Visually-Prompted Language Model for Fine-Grained Scene Graph Generation in an Open WorldCode1
Location-Free Scene Graph GenerationCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1RNNStreetMover0.03—Unverified
2GraphRNNStreetMover0.02—Unverified
3GGT without CAStreetMover0.02—Unverified
4GGTStreetMover0.02—Unverified