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 341–350 of 712 papers

TitleStatusHype
Decomposed Prototype Learning for Few-Shot Scene Graph Generation—0
A Tunable Model for Graph Generation Using LSTM and Conditional VAE—0
Generative AI for Visualization: State of the Art and Future Directions—0
Generating Triples with Adversarial Networks for Scene Graph Construction—0
DDS: Decoupled Dynamic Scene-Graph Generation Network—0
Generalized Visual Relation Detection with Diffusion Models—0
Generalized Unbiased Scene Graph Generation—0
DBGDGM: Dynamic Brain Graph Deep Generative Model—0
Attentive Relational Networks for Mapping Images to Scene Graphs—0
GEMS: Scene Expansion using Generative Models of Graphs—0
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Benchmark Results

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