论文地址:Kipf T N, Welling M. Variational graph auto-encoders[J]. NIPS, 2016. 代码地址: https://github.com/tkipf/gae图神经网络可以细分为五类:图卷积网络、图注意力网络、图时空网络、图生成网络和图自…
论文:Variational Graph Auto-Encoders阅读笔记 作者:Thomas N. Kipf, Max Welling, 和GCN的作者是一样的 会议:Bayesian Deep Learning Workshop (NIPS 2016), NIPS的一个workshop,不是长文 论文链接:Variational Graph Auto-Encoders 代码链接:tkipf/... ...
论文:Variational Graph Auto-Encoders阅读笔记 作者:Thomas N. Kipf, Max Welling, 和GCN的作者是一样的 会议:Bayesian Deep Learning Workshop (NIPS 2016), NIPS的一个workshop,不是长文 论文链接:Variational Graph Auto-Encoders 代码链接:tkipf/... 查看原文 图神经网络五大类之一 VGAE(变分图自编码器)...
Variational autoencoders (VAEs) play an important role in high-dimensional data generation based on their ability to fuse the stochastic data representation with the power of recent deep learning techniques. The main advantages of these types of generators lie in their ability to encode the informa...
Kipf, T.N., Welling, M.: Variational graph auto-encoders. NIPS Workshop on Bayesian Deep Learning. (2016) Chien, E., Peng, J., Li, P., Milenkovic, O.: Adaptive universal generalized pagerank graph neural network. In: International Conference on Learning Representations (2020) Sen, P....
Best performance combinations are located in the top right corner of the graph. On the other hand, the β hyperparameter was not as relevant as the γ hyperparameter. As expected, the β-VAE provided acceptable reconstruction fidelity results but low values of interpretability. We need to point...
论文:Variational Graph Auto-Encoders阅读笔记 论文:Variational Graph Auto-Encoders阅读笔记 作者:Thomas N. Kipf, Max Welling, 和GCN的作者是一样的 会议:Bayesian Deep Learning Workshop (NIPS 2016), NIPS的一个workshop,不是长文 论文链接:Variational Graph Auto-Encoders 代码链接:tkipf/......
论文:Variational Graph Auto-Encoders阅读笔记 作者:Thomas N. Kipf, Max Welling, 和GCN的作者是一样的 会议:Bayesian Deep Learning Workshop (NIPS 2016), NIPS的一个workshop,不是长文 论文链接:Variational Graph Auto-Encoders 代码链接:tkipf/... ...
Simonovsky M, Komodakis N (2018) GraphVAE: towards generation of small graphs using variational autoencoders. ICANN 2018, Part I. LNCS, vol 11139. Springer, Cham, pp 412–422 De Cao N, Kipf T (2018) MolGAN: an implicit generative model for small molecular graphs. ICML 2018 Workshop ...
Deep generative models for graphs are promising for being able to sidestep expensive search procedures in the huge space of chemical compounds. However, incorporating complex and non-differentiable property metrics into a generative model remains a chall