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 141–150 of 712 papers

TitleStatusHype
From Easy to Hard: Learning Curricular Shape-aware Features for Robust Panoptic Scene Graph Generation—0
3D Vessel Graph Generation Using Denoising DiffusionCode1
LTLBench: Towards Benchmarks for Evaluating Temporal Logic Reasoning in Large Language ModelsCode0
The Complexity of Symmetry Breaking Beyond Lex-Leader—0
ESGNN: Towards Equivariant Scene Graph Neural Network for 3D Scene Understanding—0
MissionGNN: Hierarchical Multimodal GNN-based Weakly Supervised Video Anomaly Recognition with Mission-Specific Knowledge Graph Generation—0
Enhanced Data Transfer Cooperating with Artificial Triplets for Scene Graph Generation—0
Cascading Large Language Models for Salient Event Graph GenerationCode0
Generative Modelling of Structurally Constrained GraphsCode1
SE-VGAE: Unsupervised Disentangled Representation Learning for Interpretable Architectural Layout Design Graph GenerationCode0
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Benchmark Results

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