論文筆記(10): Node2Vec-Scalable Feature Learning for Networks
論文筆記：Node2Vec-Scalable Feature Learning for Networks 一、簡介 node2vec是2016年提出的Graph Embedding表示方式，其訓練速度快，並開放了源碼，而且表示效果還不錯，所以挺火。本質上來說，node2vec其實是基於DeepWalk的改進，所以要想了解node2vec，就需要先了解DeepWalk。這裏對DeepWalk僅做簡要
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相關文章
- 1. node2vec: Scalable Feature Learning for Networks論文解讀
- 2. 論文閱讀筆記《Adversarial Feature Hallucination Networks for Few-Shot Learning》
- 3. [CVPR 2018 論文筆記] Feature Generating Networks for Zero-Shot Learning
- 4. [論文閱讀筆記] node2vec Scalable Feature Learning for Networks
- 5. Learning Convolutional Neural Networks for Graphs論文筆記
- 6. 論文筆記：Prototypical Networks for Few-shot Learning
- 7. C3D-Resnet C3D-Version 2 --論文筆記--ConvNet Architecture Search for Spatiotemporal Feature Learning
- 8. SuperGlue: Learning Feature Matching with Graph Neural Networks 2019 論文筆記
- 9. 論文筆記----node2vec: Scalable Feature Learning for Networks（可擴展的網絡特性學習）
- 10. 論文閱讀筆記（四十三）：Adaptive Deconvolutional Networks for Mid and High Level Feature Learning