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 541–550 of 712 papers

TitleStatusHype
Spanning Tree-based Graph Generation for Molecules—0
SrTR: Self-reasoning Transformer with Visual-linguistic Knowledge for Scene Graph Generation—0
STDG: Semi-Teacher-Student Training Paradigram for Depth-guided One-stage Scene Graph Generation—0
Streamlining Attack Tree Generation: A Fragment-Based Approach—0
Structural Plan of Indoor Scenes with Personalized Preferences—0
Studying and Improving Graph Neural Network-based Motif Estimation—0
Building Transportation Foundation Model via Generative Graph Transformer—0
Swap-based Deep Reinforcement Learning for Facility Location Problems in Networks—0
Synthesizing Diverse Network Flow Datasets with Scalable Dynamic Multigraph Generation—0
Synthetic Data for Blood Vessel Network Extraction—0
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Benchmark Results

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