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GNN论文粗读

论文

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基于异构图的GNN论文

  1. Distance Information Improves Heterogeneous Graph Neural Networks:DOI: 10.1109/TKDE.2023.3300879
    • 转导和归纳任务,创新点:异构距离编码HDE提高GNN表现能力
  2. Heterogeneous Graph Neural Network via Attribute Completion:https://dl.acm.org/doi/10.1145/3442381.3449914
    • 属性补全任务,创新点:包括拓扑嵌入的预学习和基于注意力机制的属性补全
  3. Composite Graph Neural Networks for Molecular Property Prediction:doi:10.3390/ijms25126583
    • 分类和回归任务,创新点:复合图神经网络使用多个状态更新网络处理异构图,每个网络专用于特定节点类型
    • 该文章具有回归任务,提供了使用基于异构图的GNN进行数值预测的理论与实践证明
  4. Equivariant Line Graph Neural Network for Protein-Ligand Binding Affinity Prediction:doi:10.1109/JBHI.2024.3383245
    • 结合亲和力预测(数值预测),创新点:3d坐标复合体
  5. Improving airport arrival flow prediction considering heterogeneous and dynamic network dependencies:https://doi.org/10.1016/j.inffus.2023.101924
    • 预测机场到达量,创新点:动态多图神经网络(卷积+注意力),时间感知注意力,重校准融合模块
  6. Estimating package arrival time via heterogeneous hypergraph neural network:doi:10.1016/j.eswa.2023.121740
    • 预测到达时间,创新点:利用超图解决ETA预测问题
  7. A city-based PM2.5 forecasting framework using Spatially Attentive Cluster-based Graph Neural Network model:doi:10.1016/j.jclepro.2023.137036
    • 短期PM2.5浓度,创新点:建模程序考虑了相关的气象变量
  8. Sequence pre-training-based graph neural network for predicting lncRNA-miRNA associations:doi:10.1093/bib/bbad317
    • 二分类任务

GNN领域论文

  1. Denoising AggrDegation of Graph Neural Networks by Using Principal Component Analysis:doi:10.1109/TII.2022.3156658
    • 去噪任务,创新点:dropout+PCA降低运算成本
  2. Accelerating Distributed GNN Training by Codes:doi:10.1109/TPDS.2023.3295184
    • 一般GNN任务,创新点:引入编码技术降低GNN的通信开销
  3. Dual-stream GNN fusion network for hyperspectral classification:doi:10.1007/s10489-023-04960-3
    • 分类任务,创新点:使用子立方体作为输入降低计算成本,应用了图池化、局部引导模块
  4. SCV-GNN: Sparse Compressed Vector-based Graph Neural Network Aggregation:doi:10.1109/TCAD.2023.3291672
    • 一般GNN任务,创新点:针对聚合操作优化数据结构,使用Z-Morton排序推到基于数据局部性的计算排序和分区方案
  5. Edgeless-GNN: Unsupervised Representation Learning for Edgeless Nodes:doi:10.1109/TETC.2023.3292240
    • 新节点嵌入
  6. Ha-gnn: a novel graph neural network based on hyperbolic attention:doi:10.1007/s00521-024-09689-9
    • 分类任务,创新点:将图结构映射至双曲空间或者其切线空间(HGNN),HGNN+注意力 → \rightarrow ​HA-GNN
  7. Fast prediction and control of air core in hydrocyclone by machine learning to stabilize operations:doi:10.1016/j.jece.2023.111699
    • 再现空气剖面?创新点:CFD+GNN,数据平滑,损失函数调整以纳入CFD的空气核心信息;将GNN与随机森林结合;将模型与遗传算法结合

环境领域GNN论文

  1. Urban wind field prediction based on sparse sensors and physics-informed graph-assisted auto-encoder:doi:10.1111/mice.13147
    • 风场方向预测,创新点:PINN+GNN+编码解码
    • 不做考虑
  2. A two-stage CFD-GNN approach for efficient steady-state prediction of urban airflow and airborne contaminant dispersion:doi:10.1016/j.scs.2024.105607
  • 风、阵风、污染物扩散预测,创新点:CFD提供初始状态,gnn进行后续推理从而加速运算;使用具有$k-\epsilon $模型的SRANS为GNN提供信息丰富的初始状态。
  1. A new integrated prediction method of river level based on spatiotemporal correlation:doi:10.1007/s00477-023-02617-8

    • 河流水位预测,创新点:
      1. pearson相关性分析,建立时间相关模型
      2. ChebNet(GNN)
      3. 利用AE-XGBoost重建时间特征并进行预测
  2. Nationwide Air Pollution Forecasting with Heterogeneous Graph Neural Networks:doi:10.1145/3637492

    • 空气污染预测,创新点:寄,摘要没写,代码没有
  3. A long-term prediction method for PM2.5 concentration based on spatiotemporal graph attention recurrent neural network and grey wolf optimization algorithm:doi:10.1016/j.jece.2023.111716

    • PM2.5,创新点:GWO,GAT、GNN、GRU → \rightarrow GART
  4. PM2.5 forecasting under distribution shift: A graph learning approach:https://doi.org/10.1016/j.aiopen.2023.11.001

    • https://github.com/yachuan/pm2.5forecasting
    • https://github.com/yachuan/pm2.5forecasting

原文地址:https://blog.csdn.net/weixin_44162879/article/details/140535379

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