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
Interpretable End-to-End Driving Model for Implicit Scene Understanding—0
Triple Correlations-Guided Label Supplementation for Unbiased Video Scene Graph Generation—0
Panoptic Scene Graph Generation with Semantics-Prototype LearningCode1
Addressing the Impact of Localized Training Data in Graph Neural NetworksCode0
Pixel-wise Graph Attention Networks for Person Re-identificationCode0
Disentangling Node Attributes from Graph Topology for Improved Generalizability in Link Prediction—0
Autoregressive Diffusion Model for Graph GenerationCode1
Pair then Relation: Pair-Net for Panoptic Scene Graph GenerationCode1
IntelliGraphs: Datasets for Benchmarking Knowledge Graph GenerationCode1
Unbiased Scene Graph Generation via Two-stage Causal Modeling—0
Show:102550
← PrevPage 30 of 72Next →

Benchmark Results

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