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 371–380 of 712 papers

TitleStatusHype
Conditional Diffusion Based on Discrete Graph Structures for Molecular Graph GenerationCode1
Skew Class-balanced Re-weighting for Unbiased Scene Graph GenerationCode0
Peer Learning for Unbiased Scene Graph Generation—0
EDoG: Adversarial Edge Detection For Graph Neural Networks—0
SrTR: Self-reasoning Transformer with Visual-linguistic Knowledge for Scene Graph Generation—0
Molecular Graph Generation by Decomposition and Reassembling—0
PromptCAL: Contrastive Affinity Learning via Auxiliary Prompts for Generalized Novel Category DiscoveryCode1
Data Imputation with Iterative Graph ReconstructionCode1
GraphGDP: Generative Diffusion Processes for Permutation Invariant Graph GenerationCode1
Unbiased Heterogeneous Scene Graph Generation with Relation-aware Message Passing Neural NetworkCode1
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Benchmark Results

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