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 561–570 of 712 papers

TitleStatusHype
Spanning Tree-based Graph Generation for Molecules—0
Gradient-Guided Importance Sampling for Learning Discrete Energy-Based ModelsCode0
Scene Graph Generation for Better Image Captioning?—0
BGT-Net: Bidirectional GRU Transformer Network for Scene Graph Generation—0
Adversarial Stein Training for Graph Energy Models—0
Learning of Visual Relations: The Devil is in the Tails—0
A Hard Label Black-box Adversarial Attack Against Graph Neural Networks—0
Semantic Compositional Learning for Low-shot Scene Graph Generation—0
Computing Steiner Trees using Graph Neural Networks—0
Unconditional 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