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 331–340 of 712 papers

TitleStatusHype
Deep Q-Learning for Directed Acyclic Graph Generation—0
Deep imitation learning for molecular inverse problems—0
Generative modeling of the enteric nervous system employing point pattern analysis and graph construction—0
Reviving Life on the Edge: Joint Score-Based Graph Generation of Rich Edge Attributes—0
Deep Graph Generators: A Survey—0
AUG: A New Dataset and An Efficient Model for Aerial Image Urban Scene Graph Generation—0
A Hard Label Black-box Adversarial Attack Against Graph Neural Networks—0
Generative Graph Convolutional Network for Growing Graphs—0
Deep Geometry Handling and Fragment-wise Molecular 3D Graph Generation—0
Generative Explanations for Graph Neural Network: Methods and Evaluations—0
Show:102550
← PrevPage 34 of 72Next →

Benchmark Results

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