Reference：https://mp.csdn.net/postlist
Bayesian Semi-supervised Learning with Graph Gaussian Processes
https://arxiv.org/abs/1809.04379
https://github.com/yincheng/GGP
https://www.arxiv-vanity.com/papers/1809.04379/
Deep Gaussian Embedding of Graphs: Unsupervised Inductive Learning via Ranking
圖的深度高斯嵌入：基于排序的無監(jiān)督歸納學(xué)習(xí)
https://github.com/abojchevski/graph2gauss
https://openreview.net/forum?id=r1ZdKJ-0W
Gaussian word embeddings
https://github.com/seomoz/word2gauss
圖神經(jīng)網(wǎng)絡(luò)綜述：模型與應(yīng)用
https://mp.weixin.qq.com/s?__biz=MzIwMTc4ODE0Mw==&mid=2247493906&idx=1&sn=15c9f18a1ce6baa15dc85ecb52e799f6&chksm=96ea3692a19dbf847c1711e6e194ad60d80d11138daf0938f90489a054d77cfd523bee2dc1d2&mpshare=1&scene=23&srcid=1226wPRvmg5aAHghwp9veJCP#rd
https://github.com/thunlp/GNNPapers
良心推薦：機器學(xué)習(xí)入門資料匯總及學(xué)習(xí)建議（2018版）
https://mp.weixin.qq.com/s?__biz=Mzg5NzAxMDgwNg==&mid=2247484000&idx=1&sn=92f198b840073e79e1a267d15a48a279&chksm=c0791f79f70e966fccd525bc2ecb11d328a12f566ccdc781132ffeeb41c484c1f7757db03911&mpshare=1&scene=23&srcid=1226frncJ94Rj45DDsntMPB0#rd
