We investigate graph representation learning approaches that enable models to generalize across graphs: given a model trained using the representations from one graph, our goal is to apply inference using those same model parameters when given representations computed over a new graph, unseen during model training, with minimal degradation in inference accuracy. This is in contrast to the more common task of doing inference on the unseen nodes of the same graph. We show that using random projections to estimate multiple powers of the transition matrix allows us to build a set of isomorphism-invariant features that can be used by a variety of tasks. The resulting features can be used to recover enough information about the local neighborhood of a node to enable inference with relevance competitive to other approaches while maintaining computational efficiency.

翻译：我们研究能够使模型在图间泛化的图表示学习方法：给定一个基于某张图表示训练的模型，我们的目标是当使用另一张训练期间未见的新图计算出的表示时，以最小的推理精度损失应用相同的模型参数进行推理。这与通常在相同图的未见过节点上进行推理的任务形成对比。我们证明，利用随机投影来估计转移矩阵的多个幂次，能够构建一组可用于多种任务的不变特征。这些特征能够恢复节点局部邻域的足够信息，在保持计算效率的同时实现与其他方法相当的推理准确性。