SOTAVerified

Graph Generation

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

Source: Graph Deconvolutional Generation

Papers

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Showing 31–40 of 712 papers

TitleStatusHype
Weakly Supervised Video Scene Graph Generation via Natural Language SupervisionCode1
HOG-Diff: Higher-Order Guided Diffusion for Graph GenerationCode1
RA-SGG: Retrieval-Augmented Scene Graph Generation Framework via Multi-Prototype LearningCode1
Large Language Models as Realistic Microservice Trace GeneratorsCode1
ProVision: Programmatically Scaling Vision-centric Instruction Data for Multimodal Language ModelsCode1
LayerDAG: A Layerwise Autoregressive Diffusion Model for Directed Acyclic Graph GenerationCode1
Scene Graph Generation with Role-Playing Large Language ModelsCode1
Multimodal Large Language Models for Inverse Molecular Design with Retrosynthetic PlanningCode1
HYGENE: A Diffusion-based Hypergraph Generation MethodCode1
GSDiff: Synthesizing Vector Floorplans via Geometry-enhanced Structural Graph GenerationCode1
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Benchmark Results

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