SOTAVerified

Graph Generation

Graph Generation is an important research area with significant applications in drug and material designs.

Source: Graph Deconvolutional Generation

Papers

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Showing 661–670 of 712 papers

TitleStatusHype
Haystack: A Panoptic Scene Graph Dataset to Evaluate Rare Predicate ClassesCode0
TreeFormer: Single-view Plant Skeleton Estimation via Tree-constrained Graph GenerationCode0
Environment-Invariant Curriculum Relation Learning for Fine-Grained Scene Graph GenerationCode0
Growing Story Forest Online from Massive Breaking NewsCode0
Graph-wise Common Latent Factor Extraction for Unsupervised Graph Representation LearningCode0
S^2Former-OR: Single-Stage Bi-Modal Transformer for Scene Graph Generation in ORCode0
SaGess: Sampling Graph Denoising Diffusion Model for Scalable Graph GenerationCode0
Using Motif Transitions for Temporal Graph GenerationCode0
After All, Only The Last Neuron Matters: Comparing Multi-modal Fusion Functions for Scene Graph GenerationCode0
TSPP: A Unified Benchmarking Tool for Time-series ForecastingCode0
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Benchmark Results

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