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 51–60 of 712 papers

TitleStatusHype
MM-OR: A Large Multimodal Operating Room Dataset for Semantic Understanding of High-Intensity Surgical EnvironmentsCode2
Unbiased Video Scene Graph Generation via Visual and Semantic Dual Debiasing—0
Scale-Free Graph-Language ModelsCode1
Weakly Supervised Video Scene Graph Generation via Natural Language SupervisionCode1
KnowZRel: Common Sense Knowledge-based Zero-Shot Relationship Retrieval for Generalised Scene Graph GenerationCode0
FragFM: Hierarchical Framework for Efficient Molecule Generation via Fragment-Level Discrete Flow Matching—0
Agentic Medical Knowledge Graphs Enhance Medical Question Answering: Bridging the Gap Between LLMs and Evolving Medical Knowledge—0
Private Synthetic Graph Generation and Fused Gromov-Wasserstein Distance—0
Beyond Pairwise: Global Zero-shot Temporal Graph Generation—0
Leveraging V2X for Collaborative HD Maps Construction Using Scene Graph Generation—0
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Benchmark Results

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