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 291–300 of 712 papers

TitleStatusHype
KnowZRel: Common Sense Knowledge-based Zero-Shot Relationship Retrieval for Generalised Scene Graph GenerationCode0
Interpretable Deep Graph Generation with Node-Edge Co-DisentanglementCode0
GraphGen-Redux: a Fast and Lightweight Recurrent Model for labeled Graph GenerationCode0
Disentangled Dynamic Graph Deep GenerationCode0
Input Conditioned Graph Generation for Language AgentsCode0
GraphRNN: Generating Realistic Graphs with Deep Auto-regressive ModelsCode0
BOND: Benchmarking Unsupervised Outlier Node Detection on Static Attributed GraphsCode0
GraphTune: A Learning-based Graph Generative Model with Tunable Structural FeaturesCode0
Improving Graph Generation by Restricting Graph BandwidthCode0
Instruction-Based Molecular Graph Generation with Unified Text-Graph Diffusion ModelCode0
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Benchmark Results

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