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 201–210 of 712 papers

TitleStatusHype
Towards Scene Graph AnticipationCode1
Neural Graph Generator: Feature-Conditioned Graph Generation using Latent Diffusion ModelsCode1
GraphRCG: Self-Conditioned Graph Generation—0
Graph Generation via Spectral Diffusion—0
CGGM: A conditional graph generation model with adaptive sparsity for node anomaly detection in IoT networks—0
Graph Diffusion Policy OptimizationCode1
S^2Former-OR: Single-Stage Bi-Modal Transformer for Scene Graph Generation in ORCode0
Enhancing Large Language Models with Pseudo- and Multisource- Knowledge Graphs for Open-ended Question Answering—0
ExGRG: Explicitly-Generated Relation Graph for Self-Supervised Representation Learning—0
FairWire: Fair Graph Generation—0
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Benchmark Results

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