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 121–130 of 712 papers

TitleStatusHype
HYGENE: A Diffusion-based Hypergraph Generation MethodCode1
GSDiff: Synthesizing Vector Floorplans via Geometry-enhanced Structural Graph GenerationCode1
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
Towards Flexible Visual Relationship Segmentation—0
Using Retriever Augmented Large Language Models for Attack Graph Generation—0
Random Walk Diffusion for Efficient Large-Scale Graph Generation—0
MLC-GCN: Multi-Level Generated Connectome Based GCN for AD Analysis—0
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Benchmark Results

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