SOTAVerified

Graph Generation

Graph Generation is an important research area with significant applications in drug and material designs.

Source: Graph Deconvolutional Generation

Papers

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Showing 161–170 of 712 papers

TitleStatusHype
Graph Pooling for Graph Neural Networks: Progress, Challenges, and OpportunitiesCode1
GPT-GNN: Generative Pre-Training of Graph Neural NetworksCode1
Efficient Graph Generation with Graph Recurrent Attention NetworksCode1
Efficient Initial Pose-graph Generation for Global SfMCode1
EgoExOR: An Ego-Exo-Centric Operating Room Dataset for Surgical Activity UnderstandingCode1
Location-Free Scene Graph GenerationCode1
Graph Density-Aware Losses for Novel Compositions in Scene Graph GenerationCode1
Energy-Based Learning for Scene Graph GenerationCode1
Outlining and Filling: Hierarchical Query Graph Generation for Answering Complex Questions over Knowledge GraphsCode1
Multiresolution Equivariant Graph Variational AutoencoderCode1
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Benchmark Results

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