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 1–10 of 712 papers

TitleStatusHype
4D Panoptic Scene Graph GenerationCode3
FastFlows: Flow-Based Models for Molecular Graph GenerationCode2
Boosting Knowledge Graph Generation from Tabular Data with RML ViewsCode2
DiGress: Discrete Denoising diffusion for graph generationCode2
LLM-Based Multi-Agent Systems are Scalable Graph Generative ModelsCode2
EGTR: Extracting Graph from Transformer for Scene Graph GenerationCode2
DiffGraph: Heterogeneous Graph Diffusion ModelCode2
DeFoG: Discrete Flow Matching for Graph GenerationCode2
DiscoSG: Towards Discourse-Level Text Scene Graph Parsing through Iterative Graph RefinementCode2
From Pixels to Graphs: Open-Vocabulary Scene Graph Generation with Vision-Language ModelsCode2
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Benchmark Results

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