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 351–360 of 712 papers

TitleStatusHype
Set-Aligning Framework for Auto-Regressive Event Temporal Graph GenerationCode0
SteinGen: Generating Fidelitous and Diverse Graph SamplesCode0
Cyber-Security Knowledge Graph Generation by Hierarchical Nonnegative Matrix Factorization—0
Predicate Debiasing in Vision-Language Models Integration for Scene Graph Generation Enhancement—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
GraphRCG: Self-Conditioned Graph Generation—0
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Benchmark Results

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