ReGAE：基于递归神经网络的图形自动编码器
ReGAE: Graph autoencoder based on recursive neural networks
摘要：
大型图到固定维向量（嵌入）的可逆变换仍然是一个挑战。它的克服将减少图上的任何操作到向量空间中的操作。然而，大多数现有的方法仅限于具有数十个顶点的图。在本文中，我们解决了上述挑战与递归神经网络的编码器和解码器。编码器网络将子图的嵌入转换为更大子图的嵌入，并最终转换为输入图的嵌入。解码器则相反。嵌入的维数是恒定的，与（子）图的大小无关。本文提出的模拟实验证实，我们提出的图形自动编码器，ReGAE，可以处理图形，甚至数千个顶点。
英文摘要：
Invertible transformation of large graphs into fixed dimensional vectors (embeddings) remains a challenge. Its overcoming would reduce any operation on graphs to an operation in a vector space. However, most existing methods are limited to graphs with tens of vertices. In this paper we address the above challenge 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, ReGAE, can handle graphs with even thousands of vertices.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
