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 461–470 of 712 papers

TitleStatusHype
Image-Level Attentional Context Modeling Using Nested-Graph Neural Networks—0
Image Scene Graph Generation (SGG) Benchmark—0
Importance Weighted Structure Learning for Scene Graph Generation—0
Improving global awareness of linkset predictions using Cross-Attentive Modulation tokens—0
Improving Molecular Graph Generation with Flow Matching and Optimal Transport—0
Improving Query Graph Generation for Complex Question Answering over Knowledge Base—0
Improving Scene Graph Generation with Superpixel-Based Interaction Learning—0
Predicate Debiasing in Vision-Language Models Integration for Scene Graph Generation Enhancement—0
Indoor and Outdoor 3D Scene Graph Generation via Language-Enabled Spatial Ontologies—0
Inductive Spatio-Temporal Kriging with Physics-Guided Increment Training Strategy for Air Quality Inference—0
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Benchmark Results

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