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 171–180 of 712 papers

TitleStatusHype
MAGE: Model-Level Graph Neural Networks Explanations via Motif-based Graph Generation—0
Scene Graph Generation Strategy with Co-occurrence Knowledge and Learnable Term Frequency—0
Discrete-state Continuous-time Diffusion for Graph GenerationCode1
4D Panoptic Scene Graph GenerationCode3
Exploring the Individuality and Collectivity of Intents behind Interactions for Graph Collaborative FilteringCode1
MIDGARD: Self-Consistency Using Minimum Description Length for Structured Commonsense ReasoningCode0
Hyperbolic Geometric Latent Diffusion Model for Graph GenerationCode1
Generative AI for Visualization: State of the Art and Future Directions—0
Utilizing Graph Generation for Enhanced Domain Adaptive Object Detection—0
ICST-DNET: An Interpretable Causal Spatio-Temporal Diffusion Network for Traffic Speed Prediction—0
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Benchmark Results

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