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 151–160 of 712 papers

TitleStatusHype
Accurate Learning of Graph Representations with Graph Multiset PoolingCode1
Bipartite Graph Network with Adaptive Message Passing for Unbiased Scene Graph GenerationCode1
Efficient and Degree-Guided Graph Generation via Discrete Diffusion ModelingCode1
Energy-Based Learning for Scene Graph GenerationCode1
Diffusion-based Graph Generative MethodsCode1
GraphInstruct: Empowering Large Language Models with Graph Understanding and Reasoning CapabilityCode1
Bridging Knowledge Graphs to Generate Scene GraphsCode1
Graph Neural Networks can Recover the Hidden Features Solely from the Graph StructureCode1
Autoregressive Diffusion Model for Graph GenerationCode1
Efficient Initial Pose-graph Generation for Global SfMCode1
Show:102550
← PrevPage 16 of 72Next →

Benchmark Results

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