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 491–500 of 712 papers

TitleStatusHype
A Scalable AutoML Approach Based on Graph Neural NetworksCode0
Topic Scene Graph Generation by Attention Distillation from CaptionCode1
Molecular Graph Generation via Geometric Scattering—0
CCGG: A Deep Autoregressive Model for Class-Conditional Graph Generation—0
Top-N: Equivariant set and graph generation without exchangeabilityCode0
GraphEBM: Towards Permutation Invariant and Multi-Objective Molecular Graph Generation—0
Gradient-Guided Importance Sampling for Learning Discrete Energy-Based ModelsCode0
Spanning Tree-based Graph Generation for Molecules—0
SGTR: Generating Scene Graph by Learning Compositional Triplets with Transformer—0
GraphGT: Machine Learning Datasets for Graph Generation and TransformationCode1
Show:102550
← PrevPage 50 of 72Next →

Benchmark Results

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