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 671–680 of 712 papers

TitleStatusHype
Encoding Robust Representation for Graph GenerationCode0
GraphTune: A Learning-based Graph Generative Model with Tunable Structural FeaturesCode0
Voxel Scene Graph for Intracranial HemorrhageCode0
GraphRNN: Generating Realistic Graphs with Deep Auto-regressive ModelsCode0
Taking A Closer Look at Visual Relation: Unbiased Video Scene Graph Generation with Decoupled Label LearningCode0
Edge-based sequential graph generation with recurrent neural networksCode0
GraphNVP: An Invertible Flow Model for Generating Molecular GraphsCode0
BOND: Benchmarking Unsupervised Outlier Node Detection on Static Attributed GraphsCode0
GraphGen-Redux: a Fast and Lightweight Recurrent Model for labeled Graph GenerationCode0
Scene Graph Generation from Objects, Phrases and Region CaptionsCode0
Show:102550
← PrevPage 68 of 72Next →

Benchmark Results

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