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 431–440 of 712 papers

TitleStatusHype
Beyond LLMs: A Linguistic Approach to Causal Graph Generation from Narrative Texts—0
Beyond Pairwise: Global Zero-shot Temporal Graph Generation—0
Beyond Visual Appearances: Privacy-sensitive Objects Identification via Hybrid Graph Reasoning—0
BGT-Net: Bidirectional GRU Transformer Network for Scene Graph Generation—0
VidCoM: Fast Video Comprehension through Large Language Models with Multimodal Tools—0
Bridging Scene Understanding and Task Execution with Flexible Simulation Environments—0
Bures-Wasserstein Flow Matching for Graph Generation—0
Context-aware Mixture-of-Experts for Unbiased Scene Graph Generation—0
CAT-SG: A Large Dynamic Scene Graph Dataset for Fine-Grained Understanding of Cataract Surgery—0
CCGG: A Deep Autoregressive Model for Class-Conditional Graph Generation—0
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Benchmark Results

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