Classification in biological networks withhypergraphlet kernels笔记
图学习：一般可以看作是监督的或非监督的。在监督设置下，对典型任务进行分类;即。，全图[55]标签的赋值，顶点或边分类;或link prediction中，集预测图中边的存在性。 摘要 超图是图的一种概括，它提供了一种框架来减少信息损失，并统一不同的基于图的方法。在本文中，我们提出了一种基于超图的建模物理系统的方法，并将超图上的顶点分类、边缘分类和链接预测问题表示为半监督设置中(扩展的、对偶的)超图
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相关文章
- 1. alexnet笔记（ImageNet Classification with Deep Convolutional Neural Networks）
- 2. Convolutional Neural Networks for Sentence Classification 阅读笔记
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- 4. 笔记《Graph Neural Tangent Kernel: Fusing Graph Neural Networks with Graph Kernels》-NeurIPS 2019
- 5. Label-dependent Feature Extraction in Social Networks for Node Classification
- 6. Recurrent Convolutional Neural Networks for Text Classification
- 7. PHATE：Visualizing structure and transitions in high-dimensional biological data 笔记
- 8. Neural Networks - Multi-class classification
- 9. AlexNet：ImageNet Classification with Deep Convolutional Neural Networks
- 10. 论文笔记--Appearance-and-Relation Networks for Video Classification