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 71–80 of 712 papers

TitleStatusHype
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
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
Training-free Heterogeneous Graph Condensation via Data SelectionCode0
A Deep Probabilistic Framework for Continuous Time Dynamic Graph GenerationCode0
RelationField: Relate Anything in Radiance FieldsCode2
AnalogXpert: Automating Analog Topology Synthesis by Incorporating Circuit Design Expertise into Large Language Models—0
RA-SGG: Retrieval-Augmented Scene Graph Generation Framework via Multi-Prototype LearningCode1
Large Language Models as Realistic Microservice Trace GeneratorsCode1
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Benchmark Results

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