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 361–370 of 712 papers

TitleStatusHype
Self-Supervised Relation Alignment for Scene Graph Generation—0
DBGDGM: Dynamic Brain Graph Deep Generative Model—0
Graph Neural Networks can Recover the Hidden Features Solely from the Graph StructureCode1
Improving Graph Generation by Restricting Graph BandwidthCode0
DDS: Decoupled Dynamic Scene-Graph Generation Network—0
RealGraph: A Multiview Dataset for 4D Real-world Context Graph Generation—0
Visual Traffic Knowledge Graph Generation from Scene Images—0
Fast Contextual Scene Graph Generation With Unbiased Context Augmentation—0
IS-GGT: Iterative Scene Graph Generation With Generative Transformers—0
Learning To Generate Language-Supervised and Open-Vocabulary Scene Graph Using Pre-Trained Visual-Semantic SpaceCode1
Show:102550
← PrevPage 37 of 72Next →

Benchmark Results

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