论文标题
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
论文作者
论文摘要
大图转换为固定尺寸向量（嵌入）仍然是一个挑战。它的克服将使图形上的任何操作减少到向量空间中的操作。但是，大多数现有方法仅限于具有数十个顶点的图。在本文中，我们通过递归神经网络（编码器和解码器）解决了上述挑战。编码器网络将子图的嵌入嵌入到较大子图的嵌入中，并最终转换为输入图的嵌入。解码器相反。无论（子）图的大小如何，嵌入的尺寸是恒定的。本文提出的仿真实验证实，我们提出的图形自动编码器Regae可以处理具有数千个顶点的图形。
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.
