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 271–280 of 712 papers

TitleStatusHype
ALF: Adaptive Label Finetuning for Scene Graph Generation—0
A Semi-Autoregressive Graph Generative Model for Dependency Graph Parsing—0
Agentic Medical Knowledge Graphs Enhance Medical Question Answering: Bridging the Gap Between LLMs and Evolving Medical Knowledge—0
Finding Increasingly Large Extremal Graphs with AlphaZero and Tabu Search—0
Constrained Structure Learning for Scene Graph Generation—0
A Semantic-Enhanced Heterogeneous Graph Learning Method for Flexible Objects Recognition—0
Hierarchical Memory Learning for Fine-Grained Scene Graph Generation—0
A Framework for Large Scale Synthetic Graph Dataset Generation—0
Fast Contextual Scene Graph Generation With Unbiased Context Augmentation—0
FairWire: Fair Graph Generation—0
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Benchmark Results

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