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 431–440 of 712 papers

TitleStatusHype
TransformerG2G: Adaptive time-stepping for learning temporal graph embeddings using transformersCode0
Iterative Zero-Shot LLM Prompting for Knowledge Graph Construction—0
SaGess: Sampling Graph Denoising Diffusion Model for Scalable Graph GenerationCode0
Community Detection Graph Convolutional Network for Overlap-Aware Speaker Diarization—0
Towards Unseen Triples: Effective Text-Image-joint Learning for Scene Graph Generation—0
A Semi-Autoregressive Graph Generative Model for Dependency Graph Parsing—0
Size Matters: Large Graph Generation with HiGGs—0
Using Motif Transitions for Temporal Graph GenerationCode0
Multi-Label Meta Weighting for Long-Tailed Dynamic Scene Graph GenerationCode0
On Certified Generalization in Structured Prediction—0
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Benchmark Results

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