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 1–10 of 712 papers

TitleStatusHype
4D Panoptic Scene Graph GenerationCode3
GDGB: A Benchmark for Generative Dynamic Text-Attributed Graph LearningCode2
DiscoSG: Towards Discourse-Level Text Scene Graph Parsing through Iterative Graph RefinementCode2
Open World Scene Graph Generation using Vision Language ModelsCode2
MM-OR: A Large Multimodal Operating Room Dataset for Semantic Understanding of High-Intensity Surgical EnvironmentsCode2
DiffGraph: Heterogeneous Graph Diffusion ModelCode2
RelationField: Relate Anything in Radiance FieldsCode2
LLM-Based Multi-Agent Systems are Scalable Graph Generative ModelsCode2
DeFoG: Discrete Flow Matching for Graph GenerationCode2
SkySenseGPT: A Fine-Grained Instruction Tuning Dataset and Model for Remote Sensing Vision-Language UnderstandingCode2
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Benchmark Results

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