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 331–340 of 712 papers

TitleStatusHype
Identification of vortex in unstructured mesh with graph neural networks—0
ICST-DNET: An Interpretable Causal Spatio-Temporal Diffusion Network for Traffic Speed Prediction—0
Hyper-relationship Learning Network for Scene Graph Generation—0
HyperGLM: HyperGraph for Video Scene Graph Generation and Anticipation—0
Context-aware Mixture-of-Experts for Unbiased Scene Graph Generation—0
An Interpretable Model for Scene Graph Generation—0
Efficient Dynamic Attributed Graph Generation—0
Improving Molecular Graph Generation with Flow Matching and Optimal Transport—0
Improving Query Graph Generation for Complex Question Answering over Knowledge Base—0
Hydra-SGG: Hybrid Relation Assignment for One-stage Scene Graph Generation—0
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Benchmark Results

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