Variation Autoencoder Based Network Representation Learning for Classification——paper 筆記
傳統的方法是矩陣降維和矩陣分解做爲表示，可是不適用於大型網絡。網絡 借鑑 nlp 中的處理思路，先 隨機遊走，處理成節點序列，再使用節點在上下文中的表示。缺點：沒法使用節點自身的信息。學習 本文提出一種直觀的方法是分別單獨學習文本表示和網絡結構，而後把兩種獨立的表示合併在一塊兒。3d 將網絡的鄰接矩陣和訓練好的內容表示做爲輸入，通過 VAE 獲得 embedding 表示。blog 聯合訓練模型中
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相關文章
- 1. [AAAI2018]Learning Structured Representation Representation for Text Classification via Reinforcemen
- 2. NRL: network representation learning & NE: network embedding. 相關論文總結
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- 8. CVPR 2017 Deep representation learning for human motion prediction and classification 論文解讀
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- 10. 論文筆記《Cross-Language Learning for Program Classification using Bilateral Tree-Based Convolutional Ne