DeepMind 开源图神经网络的代码

用于支持论文《Relational inductive biases, deep learning, and graph networks》。

github

A graph network takes a graph as input and returns a graph as output. The input graph has edge- (E ), node- (V ), and global-level (u) attributes. The output graph has the same structure, but updated attributes. Graph networks are part of the broader family of "graph neural networks" (Scarselli et al., 2009).

讲直白一些，就是用神经网络处理图，输入是图，输出也是图。之前都是处理向量(Vector)，因此NLP中须要作Word2Vec后才能运用深度学习的处理结果。做者们认为 Graph2Graph 是让神经网络具有推理(Reason)能力的一个关键步骤。

【CNN已老，GNN来了】DeepMind、谷歌大脑、MIT等27位做者重磅论文，图网络让深度学习也能因果推理