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 61–70 of 712 papers

TitleStatusHype
Efficient Initial Pose-graph Generation for Global SfMCode1
Efficient Graph Generation with Graph Recurrent Attention NetworksCode1
Compositional Feature Augmentation for Unbiased Scene Graph GenerationCode1
3M-Diffusion: Latent Multi-Modal Diffusion for Language-Guided Molecular Structure GenerationCode1
GSDiff: Synthesizing Vector Floorplans via Geometry-enhanced Structural Graph GenerationCode1
CogTree: Cognition Tree Loss for Unbiased Scene Graph GenerationCode1
Exploring the Individuality and Collectivity of Intents behind Interactions for Graph Collaborative FilteringCode1
Bridging Knowledge Graphs to Generate Scene GraphsCode1
ExplaGraphs: An Explanation Graph Generation Task for Structured Commonsense ReasoningCode1
Energy-Based Learning for Scene Graph GenerationCode1
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Benchmark Results

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