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 51–60 of 712 papers

TitleStatusHype
Adaptive Self-training Framework for Fine-grained Scene Graph GenerationCode1
A Simple and Scalable Representation for Graph GenerationCode1
A Graph is Worth K Words: Euclideanizing Graph using Pure TransformerCode1
Conditional Diffusion Based on Discrete Graph Structures for Molecular Graph GenerationCode1
EgoExOR: An Ego-Exo-Centric Operating Room Dataset for Surgical Activity UnderstandingCode1
Bridging Knowledge Graphs to Generate Scene GraphsCode1
Exploring the Individuality and Collectivity of Intents behind Interactions for Graph Collaborative FilteringCode1
DIFFVSGG: Diffusion-Driven Online Video Scene Graph GenerationCode1
Directed Graph Grammars for Sequence-based LearningCode1
CogTree: Cognition Tree Loss for Unbiased Scene Graph GenerationCode1
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Benchmark Results

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