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 176–200 of 712 papers

TitleStatusHype
MIDGARD: Self-Consistency Using Minimum Description Length for Structured Commonsense ReasoningCode0
Hyperbolic Geometric Latent Diffusion Model for Graph GenerationCode1
Generative AI for Visualization: State of the Art and Future Directions—0
Utilizing Graph Generation for Enhanced Domain Adaptive Object Detection—0
ICST-DNET: An Interpretable Causal Spatio-Temporal Diffusion Network for Traffic Speed Prediction—0
Accelerating Medical Knowledge Discovery through Automated Knowledge Graph Generation and Enrichment—0
A Review and Efficient Implementation of Scene Graph Generation MetricsCode1
Tri-modal Confluence with Temporal Dynamics for Scene Graph Generation in Operating Rooms—0
AUG: A New Dataset and An Efficient Model for Aerial Image Urban Scene Graph Generation—0
ORacle: Large Vision-Language Models for Knowledge-Guided Holistic OR Domain ModelingCode1
SportsHHI: A Dataset for Human-Human Interaction Detection in Sports VideosCode1
Weakly-Supervised 3D Scene Graph Generation via Visual-Linguistic Assisted Pseudo-labelingCode0
EGTR: Extracting Graph from Transformer for Scene Graph GenerationCode2
Set-Aligning Framework for Auto-Regressive Event Temporal Graph GenerationCode0
From Pixels to Graphs: Open-Vocabulary Scene Graph Generation with Vision-Language ModelsCode2
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