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 41–50 of 712 papers

TitleStatusHype
Combinatorial Complex Score-based Diffusion Modelling through Stochastic Differential EquationsCode1
Dual-branch Hybrid Learning Network for Unbiased Scene Graph GenerationCode1
Bridging Knowledge Graphs to Generate Scene GraphsCode1
Adaptive Graph Convolutional Recurrent Network for Traffic ForecastingCode1
A Fair Ranking and New Model for Panoptic Scene Graph GenerationCode1
Are scene graphs good enough to improve Image Captioning?Code1
CARE: Causality Reasoning for Empathetic Responses by Conditional Graph GenerationCode1
A Review and Efficient Implementation of Scene Graph Generation MetricsCode1
Context-Aware Scene Graph Generation With Seq2Seq TransformersCode1
Compositional Feature Augmentation for Unbiased Scene Graph GenerationCode1
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Benchmark Results

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