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 191–200 of 712 papers

TitleStatusHype
GPT-GNN: Generative Pre-Training of Graph Neural NetworksCode1
Expressive Scene Graph Generation Using Commonsense Knowledge Infusion for Visual Understanding and ReasoningCode1
Compositional Feature Augmentation for Unbiased Scene Graph GenerationCode1
Face Super-Resolution Using Stochastic Differential EquationsCode1
A Review and Efficient Implementation of Scene Graph Generation MetricsCode1
Graph Neural Networks can Recover the Hidden Features Solely from the Graph StructureCode1
Conditional Diffusion Based on Discrete Graph Structures for Molecular Graph GenerationCode1
Large Language Models for Constrained-Based Causal DiscoveryCode1
Prototype-based Embedding Network for Scene Graph GenerationCode1
Visual Graphs from Motion (VGfM): Scene understanding with object geometry reasoningCode1
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Benchmark Results

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