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
Adaptive Self-training Framework for Fine-grained Scene Graph GenerationCode1
A Simple and Scalable Representation for Graph GenerationCode1
A Graph is Worth K Words: Euclideanizing Graph using Pure TransformerCode1
Efficient and Degree-Guided Graph Generation via Discrete Diffusion ModelingCode1
Fine-Grained Scene Graph Generation with Data TransferCode1
Formal Query Building with Query Structure Prediction for Complex Question Answering over Knowledge BaseCode1
Efficient Graph Generation with Graph Recurrent Attention NetworksCode1
CARE: Causality Reasoning for Empathetic Responses by Conditional Graph GenerationCode1
Efficient Initial Pose-graph Generation for Global SfMCode1
AutoKG: Efficient Automated Knowledge Graph Generation for Language ModelsCode1
Generative Modelling of Structurally Constrained GraphsCode1
Expanding Scene Graph Boundaries: Fully Open-vocabulary Scene Graph Generation via Visual-Concept Alignment and RetentionCode1
Dirichlet Graph Variational AutoencoderCode1
Advancing Graph Generation through Beta DiffusionCode1
Biasing Like Human: A Cognitive Bias Framework for Scene Graph GenerationCode1
DIFFVSGG: Diffusion-Driven Online Video Scene Graph GenerationCode1
Directed Graph Grammars for Sequence-based LearningCode1
Discrete-state Continuous-time Diffusion for Graph GenerationCode1
Data Imputation with Iterative Graph ReconstructionCode1
Dense Relational Image Captioning via Multi-task Triple-Stream NetworksCode1
GSDiff: Synthesizing Vector Floorplans via Geometry-enhanced Structural Graph GenerationCode1
Diffusion-based Graph Generative MethodsCode1
Bipartite Graph Network with Adaptive Message Passing for Unbiased Scene Graph GenerationCode1
3M-Diffusion: Latent Multi-Modal Diffusion for Language-Guided Molecular Structure GenerationCode1
CycleGT: Unsupervised Graph-to-Text and Text-to-Graph Generation via Cycle TrainingCode1
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Benchmark Results

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