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 291–300 of 712 papers

TitleStatusHype
Computing Steiner Trees using Graph Neural Networks—0
Identification of vortex in unstructured mesh with graph neural networks—0
Community Detection Graph Convolutional Network for Overlap-Aware Speaker Diarization—0
Target-Tailored Source-Transformation for Scene Graph Generation—0
A Reverse Causal Framework to Mitigate Spurious Correlations for Debiasing Scene Graph Generation—0
Image-Level Attentional Context Modeling Using Nested-Graph Neural Networks—0
Improving global awareness of linkset predictions using Cross-Attentive Modulation tokens—0
Improving Scene Graph Generation with Superpixel-Based Interaction Learning—0
Instant Policy: In-Context Imitation Learning via Graph Diffusion—0
Exploring the Potential of Large Language Models in Graph Generation—0
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Benchmark Results

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