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 301–310 of 712 papers

TitleStatusHype
Metric-Semantic Factor Graph Generation based on Graph Neural Networks—0
Point2Graph: An End-to-end Point Cloud-based 3D Open-Vocabulary Scene Graph for Robot Navigation—0
Hydra-SGG: Hybrid Relation Assignment for One-stage Scene Graph Generation—0
Training-Free Guidance for Discrete Diffusion Models for Molecular Generation—0
Towards Localizing Structural Elements: Merging Geometrical Detection with Semantic Verification in RGB-D Data—0
A Modern Take on Visual Relationship Reasoning for Grasp Planning—0
SiHGNN: Leveraging Properties of Semantic Graphs for Efficient HGNN Acceleration—0
Ensemble Predicate Decoding for Unbiased Scene Graph Generation—0
Focused Discriminative Training For Streaming CTC-Trained Automatic Speech Recognition Models—0
Instruction-Based Molecular Graph Generation with Unified Text-Graph Diffusion ModelCode0
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Benchmark Results

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