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 51–75 of 712 papers

TitleStatusHype
OED: Towards One-stage End-to-End Dynamic Scene Graph GenerationCode1
Discrete-state Continuous-time Diffusion for Graph GenerationCode1
Exploring the Individuality and Collectivity of Intents behind Interactions for Graph Collaborative FilteringCode1
Hyperbolic Geometric Latent Diffusion Model for Graph GenerationCode1
A Review and Efficient Implementation of Scene Graph Generation MetricsCode1
ORacle: Large Vision-Language Models for Knowledge-Guided Holistic OR Domain ModelingCode1
SportsHHI: A Dataset for Human-Human Interaction Detection in Sports VideosCode1
3M-Diffusion: Latent Multi-Modal Diffusion for Language-Guided Molecular Structure GenerationCode1
GraphInstruct: Empowering Large Language Models with Graph Understanding and Reasoning CapabilityCode1
Towards Scene Graph AnticipationCode1
Neural Graph Generator: Feature-Conditioned Graph Generation using Latent Diffusion ModelsCode1
Graph Diffusion Policy OptimizationCode1
Pard: Permutation-Invariant Autoregressive Diffusion for Graph GenerationCode1
A Graph is Worth K Words: Euclideanizing Graph using Pure TransformerCode1
Diffusion-based Graph Generative MethodsCode1
Adaptive Self-training Framework for Fine-grained Scene Graph GenerationCode1
Efficient and Scalable Graph Generation through Iterative Local ExpansionCode1
A Simple and Scalable Representation for Graph GenerationCode1
Panoptic Video Scene Graph GenerationCode1
VLPrompt: Vision-Language Prompting for Panoptic Scene Graph GenerationCode1
AutoKG: Efficient Automated Knowledge Graph Generation for Language ModelsCode1
Expanding Scene Graph Boundaries: Fully Open-vocabulary Scene Graph Generation via Visual-Concept Alignment and RetentionCode1
NeuSyRE: Neuro-Symbolic Visual Understanding and Reasoning Framework based on Scene Graph EnrichmentCode1
Sparse Training of Discrete Diffusion Models for Graph GenerationCode1
GraphMaker: Can Diffusion Models Generate Large Attributed Graphs?Code1
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Benchmark Results

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