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
OED: Towards One-stage End-to-End Dynamic Scene Graph GenerationCode1
Discrete-state Continuous-time Diffusion for Graph GenerationCode1
Exploring the Individuality and Collectivity of Intents behind Interactions for Graph Collaborative FilteringCode1
Hyperbolic Geometric Latent Diffusion Model for Graph GenerationCode1
A Review and Efficient Implementation of Scene Graph Generation MetricsCode1
ORacle: Large Vision-Language Models for Knowledge-Guided Holistic OR Domain ModelingCode1
SportsHHI: A Dataset for Human-Human Interaction Detection in Sports VideosCode1
3M-Diffusion: Latent Multi-Modal Diffusion for Language-Guided Molecular Structure GenerationCode1
GraphInstruct: Empowering Large Language Models with Graph Understanding and Reasoning CapabilityCode1
Towards Scene Graph AnticipationCode1
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Benchmark Results

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