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 321–330 of 712 papers

TitleStatusHype
From Easy to Hard: Learning Curricular Shape-aware Features for Robust Panoptic Scene Graph Generation—0
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
SE-VGAE: Unsupervised Disentangled Representation Learning for Interpretable Architectural Layout Design Graph GenerationCode0
Beyond Visual Appearances: Privacy-sensitive Objects Identification via Hybrid Graph Reasoning—0
Input Conditioned Graph Generation for Language AgentsCode0
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Benchmark Results

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