Subgraph GNNs are provably expressive neural architectures that learn graph
representations from sets of subgraphs. Unfortunately, their applicability is
hampered by the computational complexity associated with performing message
passing on many subgraphs. In this paper, we consider the problem of learning
to select a small subset of the large set of possible subgraphs in a
data-driven fashion. We first motivate the problem by proving that there are
families of WL-indistinguishable graphs for which there exist efficient
subgraph selection policies: small subsets of subgraphs that can already
identify all the graphs within the family. We then propose a new approach,
called Policy-Learn, that learns how to select subgraphs in an iterative
manner. We prove that, unlike popular random policies and prior work addressing
the same problem, our architecture is able to learn the efficient policies
mentioned above. Our experimental results demonstrate that Policy-Learn
outperforms existing baselines across a wide range of datasets.

我们提出了一种名为 Policy-Learn 的新方法，该方法通过学习如何选择子图解决了学习从大量可能的子图中选择一个小子图集的问题，并在实验结果中表现优于现有的基线模型。