SOTAVerified

Graph Generation

Graph Generation is an important research area with significant applications in drug and material designs.

Source: Graph Deconvolutional Generation

Papers

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Showing 701–712 of 712 papers

TitleStatusHype
Counterfactual Critic Multi-Agent Training for Scene Graph Generation—0
Scene Graph Generation: A Comprehensive Survey—0
Scene Graph Generation for Better Image Captioning?—0
Scene Graph Generation Strategy with Co-occurrence Knowledge and Learnable Term Frequency—0
Scene Graph Generation via Conditional Random Fields—0
Scene Graph Generation with External Knowledge and Image Reconstruction—0
Scene Graph Generation with Geometric Context—0
SceneLLM: Implicit Language Reasoning in LLM for Dynamic Scene Graph Generation—0
SCGG: A Deep Structure-Conditioned Graph Generative Model—0
Score-based Generative Models with Adaptive Momentum—0
SeaDAG: Semi-autoregressive Diffusion for Conditional Directed Acyclic Graph Generation—0
Secure Network Release with Link Privacy—0
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Benchmark Results

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