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 211–220 of 712 papers

TitleStatusHype
Pard: Permutation-Invariant Autoregressive Diffusion for Graph GenerationCode1
Reviving Life on the Edge: Joint Score-Based Graph Generation of Rich Edge Attributes—0
Overcoming Order in Autoregressive Graph Generation—0
A Graph is Worth K Words: Euclideanizing Graph using Pure TransformerCode1
Diffusion-based Graph Generative MethodsCode1
Towards Lifelong Scene Graph Generation with Knowledge-ware In-context Prompt Learning—0
Is 3-(F)WL Enough to Distinguish All 3D Graphs?—0
Interpreting Equivariant Representations—0
SGTR+: End-to-end Scene Graph Generation with TransformerCode2
TD^2-Net: Toward Denoising and Debiasing for Dynamic Scene Graph Generation—0
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Benchmark Results

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