Graphae: Adaptive Embedding Across Graphs

Recently, learning embedding of nodes in graphs has attracted increasing research attention. There are two main kinds of graph embedding methods, i.e., the transductive embedding methods and the inductive embedding methods. The former focuses on directly optimizing the embedding vectors, and the latter tries to learn a mapping function for the given nodes and features. However, few works focus on applying the learned model from one graph to another, which is a pervasive idea in Computer Version or Natural Language Processing. Although some of the graph neural networks (GNNs) present similar motivation, none of them considers the graph bias among graphs. In this paper, we present an interesting graph embedding problem called Adaptive Task (AT), and propose a unified framework for this adaptive task, which introduces two types of alignment to learn adaptive node embedding across graphs. Then, based on the proposed framework, a novel graph adaptive embedding network is designed to address the adaptive task. Extensive experimental results demonstrate that our model significantly outperforms the state-of-the-art methods.