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 651–660 of 712 papers

TitleStatusHype
NetDiff: Deep Graph Denoising Diffusion for Ad Hoc Network Topology Generation—0
Network Generation with Differential Privacy—0
Neural Belief Propagation for Scene Graph Generation—0
Neuro-Symbolic Scene Graph Conditioning for Synthetic Image Dataset Generation—0
Neuro-Symbolic Visual Reasoning: Disentangling "Visual" from "Reasoning"—0
NGTM: Substructure-based Neural Graph Topic Model for Interpretable Graph Generation—0
NICEST: Noisy Label Correction and Training for Robust Scene Graph Generation—0
Not All Relations are Equal: Mining Informative Labels for Scene Graph Generation—0
NuScenes-SpatialQA: A Spatial Understanding and Reasoning Benchmark for Vision-Language Models in Autonomous Driving—0
NVDiff: Graph Generation through the Diffusion of Node Vectors—0
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Benchmark Results

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