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 261–270 of 712 papers

TitleStatusHype
Evolving Hard Maximum Cut Instances for Quantum Approximate Optimization Algorithms—0
UniQ: Unified Decoder with Task-specific Queries for Efficient Scene Graph Generation—0
Graph Generative Pre-trained Transformer—0
Hybrid Reciprocal Transformer with Triplet Feature Alignment for Scene Graph Generation—0
Navigating the Unseen: Zero-shot Scene Graph Generation via Capsule-Based Equivariant Features—0
GraphMimic: Graph-to-Graphs Generative Modeling from Videos for Policy Learning—0
Learning 4D Panoptic Scene Graph Generation from Rich 2D Visual Scene—0
Relation-aware Hierarchical Prompt for Open-vocabulary Scene Graph Generation—0
KG4Diagnosis: A Hierarchical Multi-Agent LLM Framework with Knowledge Graph Enhancement for Medical Diagnosis—0
A Deep Probabilistic Framework for Continuous Time Dynamic Graph GenerationCode0
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Benchmark Results

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