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 131–140 of 712 papers

TitleStatusHype
Dirichlet Graph Variational AutoencoderCode1
Directed Graph Grammars for Sequence-based LearningCode1
HL-Net: Heterophily Learning Network for Scene Graph GenerationCode1
Dual-branch Hybrid Learning Network for Unbiased Scene Graph GenerationCode1
HOG-Diff: Higher-Order Guided Diffusion for Graph GenerationCode1
DIFFVSGG: Diffusion-Driven Online Video Scene Graph GenerationCode1
Discrete-state Continuous-time Diffusion for Graph GenerationCode1
Hyperbolic Geometric Latent Diffusion Model for Graph GenerationCode1
Efficient and Scalable Graph Generation through Iterative Local ExpansionCode1
Accurate Learning of Graph Representations with Graph Multiset PoolingCode1
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Benchmark Results

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