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 211–220 of 712 papers

TitleStatusHype
A Causal Adjustment Module for Debiasing Scene Graph Generation—0
DEFactor: Differentiable Edge Factorization-based Probabilistic Graph Generation—0
Deep Q-Learning for Directed Acyclic Graph Generation—0
Deep imitation learning for molecular inverse problems—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
Gransformer: Transformer-based Graph Generation—0
Deep Geometry Handling and Fragment-wise Molecular 3D Graph Generation—0
Decomposed Prototype Learning for Few-Shot Scene Graph Generation—0
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Benchmark Results

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