Invertible transformation of large graphs into constant dimensional vectors (embeddings) remains a challenge. In this paper we address it with recursive neural networks: The encoder and the decoder. The encoder network transforms embeddings of subgraphs into embeddings of larger subgraphs, and eventually into the embedding of the input graph. The decoder does the opposite. The dimension of the embeddings is constant regardless of the size of the (sub)graphs. Simulation experiments presented in this paper confirm that our proposed graph autoencoder can handle graphs with even thousands of vertices.

翻译：将大图向恒定维向矢量( 堆积) 进行不可逆的转换仍是一项挑战。 在本文中, 我们用循环神经网络来解决这个问题: 编码器和解码器。 编码器网络将子图的嵌入转换成较大子图的嵌入, 并最终转化为输入图的嵌入。 解码器正好相反。 嵌入的维度是不变的, 不论( 子) 的大小。 本文中提供的模拟实验证实, 我们提议的图形自动编码器可以用甚至数千个顶部处理图形 。