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 141–150 of 712 papers

TitleStatusHype
Expanding Scene Graph Boundaries: Fully Open-vocabulary Scene Graph Generation via Visual-Concept Alignment and RetentionCode1
Energy-Based Learning for Scene Graph GenerationCode1
GSDiff: Synthesizing Vector Floorplans via Geometry-enhanced Structural Graph GenerationCode1
Explanation Graph Generation via Pre-trained Language Models: An Empirical Study with Contrastive LearningCode1
IntelliGraphs: Datasets for Benchmarking Knowledge Graph GenerationCode1
Exploring the Individuality and Collectivity of Intents behind Interactions for Graph Collaborative FilteringCode1
DIFFVSGG: Diffusion-Driven Online Video Scene Graph GenerationCode1
Dual-branch Hybrid Learning Network for Unbiased Scene Graph GenerationCode1
3M-Diffusion: Latent Multi-Modal Diffusion for Language-Guided Molecular Structure GenerationCode1
Accurate Learning of Graph Representations with Graph Multiset PoolingCode1
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Benchmark Results

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