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 526–550 of 712 papers

TitleStatusHype
Self-Supervised Relation Alignment for Scene Graph Generation—0
Semantic Compositional Learning for Low-shot Scene Graph Generation—0
Semantic Scene Graph Generation Based on an Edge Dual Scene Graph and Message Passing Neural Network—0
Semantic Similarity Score for Measuring Visual Similarity at Semantic Level—0
Semantic Structure based Query Graph Prediction for Question Answering over Knowledge Graph—0
SGRAM: Improving Scene Graph Parsing via Abstract Meaning Representation—0
SG-Shuffle: Multi-aspect Shuffle Transformer for Scene Graph Generation—0
SGTR: Generating Scene Graph by Learning Compositional Triplets with Transformer—0
SHADOWCAST: Controllable Graph Generation with Explainability—0
SHADOWCAST: Controllable Graph Generation—0
SiHGNN: Leveraging Properties of Semantic Graphs for Efficient HGNN Acceleration—0
Sim2SG: Sim-to-Real Scene Graph Generation for Transfer Learning—0
Self-Supervised Real-to-Sim Scene Generation—0
Single-Stage Visual Relationship Learning using Conditional Queries—0
SPADE: Spatial-Aware Denoising Network for Open-vocabulary Panoptic Scene Graph Generation with Long- and Local-range Context Reasoning—0
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