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 276–300 of 712 papers

TitleStatusHype
Agentic Medical Knowledge Graphs Enhance Medical Question Answering: Bridging the Gap Between LLMs and Evolving Medical Knowledge—0
Improving Query Graph Generation for Complex Question Answering over Knowledge Base—0
Image Scene Graph Generation (SGG) Benchmark—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
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
Conformal Prediction and MLLM aided Uncertainty Quantification in Scene Graph Generation—0
Evaluating the Cybersecurity Risk of Real World, Machine Learning Production Systems—0
Importance Weighted Structure Learning for Scene Graph Generation—0
Fairness Amidst Non-IID Graph Data: A Literature Review—0
FairGen: Towards Fair Graph Generation—0
FactReranker: Fact-guided Reranker for Faithful Radiology Report Summarization—0
Computing Steiner Trees using Graph Neural Networks—0
Identification of vortex in unstructured mesh with graph neural networks—0
Community Detection Graph Convolutional Network for Overlap-Aware Speaker Diarization—0
Target-Tailored Source-Transformation for Scene Graph Generation—0
A Reverse Causal Framework to Mitigate Spurious Correlations for Debiasing Scene Graph Generation—0
Image-Level Attentional Context Modeling Using Nested-Graph Neural Networks—0
Improving global awareness of linkset predictions using Cross-Attentive Modulation tokens—0
Improving Scene Graph Generation with Superpixel-Based Interaction Learning—0
Instant Policy: In-Context Imitation Learning via Graph Diffusion—0
Exploring the Potential of Large Language Models in Graph Generation—0
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Benchmark Results

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