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 451–460 of 712 papers

TitleStatusHype
HIG: Hierarchical Interlacement Graph Approach to Scene Graph Generation in Video Understanding—0
HMGIE: Hierarchical and Multi-Grained Inconsistency Evaluation for Vision-Language Data Cleansing—0
HOIverse: A Synthetic Scene Graph Dataset With Human Object Interactions—0
HOSE-Net: Higher Order Structure Embedded Network for Scene Graph Generation—0
Hybrid Reciprocal Transformer with Triplet Feature Alignment for Scene Graph Generation—0
Hydra-SGG: Hybrid Relation Assignment for One-stage Scene Graph Generation—0
HyperGLM: HyperGraph for Video Scene Graph Generation and Anticipation—0
Hyper-relationship Learning Network for Scene Graph Generation—0
ICST-DNET: An Interpretable Causal Spatio-Temporal Diffusion Network for Traffic Speed Prediction—0
Identification of vortex in unstructured mesh with graph neural networks—0
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Benchmark Results

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