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 81–90 of 712 papers

TitleStatusHype
RLIPv2: Fast Scaling of Relational Language-Image Pre-trainingCode1
Vision Relation Transformer for Unbiased Scene Graph GenerationCode1
Compositional Feature Augmentation for Unbiased Scene Graph GenerationCode1
Panoptic Scene Graph Generation with Semantics-Prototype LearningCode1
Pair then Relation: Pair-Net for Panoptic Scene Graph GenerationCode1
Autoregressive Diffusion Model for Graph GenerationCode1
IntelliGraphs: Datasets for Benchmarking Knowledge Graph GenerationCode1
SwinGNN: Rethinking Permutation Invariance in Diffusion Models for Graph GenerationCode1
Manga109Dialog: A Large-scale Dialogue Dataset for Comics Speaker DetectionCode1
Hyperbolic Graph Diffusion ModelCode1
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Benchmark Results

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