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 11–20 of 712 papers

TitleStatusHype
Open World Scene Graph Generation using Vision Language ModelsCode2
Representing Time-Continuous Behavior of Cyber-Physical Systems in Knowledge Graphs—0
Out-of-Distribution Graph Models Merging—0
Studying and Improving Graph Neural Network-based Motif Estimation—0
Hi-Dyna Graph: Hierarchical Dynamic Scene Graph for Robotic Autonomy in Human-Centric Environments—0
EgoExOR: An Ego-Exo-Centric Operating Room Dataset for Surgical Activity UnderstandingCode1
Directed Graph Grammars for Sequence-based LearningCode1
A Reverse Causal Framework to Mitigate Spurious Correlations for Debiasing Scene Graph Generation—0
Graph Guided Diffusion: Unified Guidance for Conditional Graph Generation—0
From Data to Modeling: Fully Open-vocabulary 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