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
HOG-Diff: Higher-Order Guided Diffusion for Graph GenerationCode1
Flatten Graphs as Sequences: Transformers are Scalable Graph Generators—0
Towards Fast Graph Generation via Autoregressive Noisy Filtration ModelingCode0
Do Graph Diffusion Models Accurately Capture and Generate Substructure Distributions?—0
Evolving Hard Maximum Cut Instances for Quantum Approximate Optimization Algorithms—0
UniQ: Unified Decoder with Task-specific Queries for Efficient Scene Graph Generation—0
DiffGraph: Heterogeneous Graph Diffusion ModelCode2
Graph Generative Pre-trained Transformer—0
Learning 4D Panoptic Scene Graph Generation from Rich 2D Visual Scene—0
Navigating the Unseen: Zero-shot Scene Graph Generation via Capsule-Based Equivariant Features—0
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Benchmark Results

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