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 241–250 of 712 papers

TitleStatusHype
RetroDiff: Retrosynthesis as Multi-stage Distribution Interpolation—0
AutoKG: Efficient Automated Knowledge Graph Generation for Language ModelsCode1
Exploring Graph Classification Techniques Under Low Data Constraints: A Comprehensive Study—0
Expanding Scene Graph Boundaries: Fully Open-vocabulary Scene Graph Generation via Visual-Concept Alignment and RetentionCode1
Two Stream Scene Understanding on Graph Embedding—0
Identification of vortex in unstructured mesh with graph neural networks—0
Generative Explanations for Graph Neural Network: Methods and Evaluations—0
Finding Increasingly Large Extremal Graphs with AlphaZero and Tabu Search—0
NeuSyRE: Neuro-Symbolic Visual Understanding and Reasoning Framework based on Scene Graph EnrichmentCode1
Sparse Training of Discrete Diffusion Models for Graph GenerationCode1
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Benchmark Results

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