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 131–140 of 712 papers

TitleStatusHype
Dirichlet Graph Variational AutoencoderCode1
Directed Graph Grammars for Sequence-based LearningCode1
Efficient Initial Pose-graph Generation for Global SfMCode1
Efficient and Scalable Graph Generation through Iterative Local ExpansionCode1
Efficient Graph Generation with Graph Recurrent Attention NetworksCode1
Energy-Based Learning for Scene Graph GenerationCode1
Discrete-state Continuous-time Diffusion for Graph GenerationCode1
ExplaGraphs: An Explanation Graph Generation Task for Structured Commonsense ReasoningCode1
EgoExOR: An Ego-Exo-Centric Operating Room Dataset for Surgical Activity UnderstandingCode1
DIFFVSGG: Diffusion-Driven Online Video Scene Graph GenerationCode1
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Benchmark Results

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