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 431–440 of 712 papers

TitleStatusHype
Graph Generation with Variational Recurrent Neural Network—0
Graph Generative Pre-trained Transformer—0
Graph Generators: State of the Art and Open Challenges—0
Graph Guided Diffusion: Unified Guidance for Conditional Graph Generation—0
GraphMapper: Efficient Visual Navigation by Scene Graph Generation—0
GraphMimic: Graph-to-Graphs Generative Modeling from Videos for Policy Learning—0
GraphNVP: an Invertible Flow-based Model for Generating Molecular Graphs—0
Graph Polish: A Novel Graph Generation Paradigm for Molecular Optimization—0
GraphRCG: Self-Conditioned Graph Generation—0
Graph Residual Flow for Molecular Graph Generation—0
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Benchmark Results

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