Survey

• [2022 TVCG]A Survey on ML4VIS- Applying Machine Learning Advances to Data Visualization

Deep Learning for Graph Visualization

• [2018 TVCG]What Would a Graph Look Like in This Layout? A Machine Learning Approach to Large Graph Visualization
• [2019 TVCG]A Deep Generative Model for Graph Layout
• [2020 TVCG]DeepDrawing- A Deep Learning Approach to Graph Drawinghttps://ieeexplore.ieee.org/abstract/document/8807275?casa_token=RkkK42yyhp8AAAAA:LiJ_lA_1U7Nm2V9QwwykJowgCA3YsIAb0yRC2CA4RSgUdtKX5_4Klnl8DHJZJohj85w7qcbYl-s
• [2021 GD]Deep Neural Network for DrawiNg Networks, (DNN)^2https://link.springer.com/chapter/10.1007/978-3-030-92931-2_27
  • [2024 TVCG]Toward Efficient Deep Learning for Graph Drawing (DL4GD) https://ieeexplore.ieee.org/document/9950620
• [2021 CG&A]DeepGD- A Deep Learning Framework for Graph Drawing Using GNN
• [2022 TNNLS]Graph Neural Networks for Graph Drawing
• [2023 TVCG]SmartGD- A GAN-Based Graph Drawing Framework for Diverse Aesthetic Goals
• [2023 EuroVis]Ask and You Shall Receive (a Graph Drawing)https://diglib.eg.org/bitstream/handle/10.2312/evs20231047/079-083.pdf
• [2023 Nature communications]Accelerating network layouts using graph neural networks

GNN for Samples Visualization

• [2020 CIKM]GNNVIs- Visualize large-scale data by learning a graph neural network representation

Data and Evaluation

• https://visdunneright.github.io/gd_benchmark_sets/
• https://github.com/VisDunneRight/gd_benchmark_sets
• [2024 CGF]Evaluating Graph Layout Algorithms- A Systematic Review of Methods and Best Practices