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 191–200 of 712 papers

TitleStatusHype
SteinGen: Generating Fidelitous and Diverse Graph SamplesCode0
Predicate Debiasing in Vision-Language Models Integration for Scene Graph Generation Enhancement—0
Cyber-Security Knowledge Graph Generation by Hierarchical Nonnegative Matrix Factorization—0
DSGG: Dense Relation Transformer for an End-to-end Scene Graph GenerationCode0
Exploring the Potential of Large Language Models in Graph Generation—0
Graphs Unveiled: Graph Neural Networks and Graph Generation—0
Deep Geometry Handling and Fragment-wise Molecular 3D Graph Generation—0
Mapping High-level Semantic Regions in Indoor Environments without Object Recognition—0
3M-Diffusion: Latent Multi-Modal Diffusion for Language-Guided Molecular Structure GenerationCode1
GraphInstruct: Empowering Large Language Models with Graph Understanding and Reasoning CapabilityCode1
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Benchmark Results

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