An effective aggregation of node features into a graph-level representation via readout functions is an essential step in numerous learning tasks involving graph neural networks. Typically, readouts are simple and non-adaptive functions designed such that the resulting hypothesis space is permutation invariant. Prior work on deep sets indicates that such readouts might require complex node embeddings that can be difficult to learn via standard neighborhood aggregation schemes. Motivated by this, we investigate the potential of adaptive readouts given by neural networks that do not necessarily give rise to permutation invariant hypothesis spaces. We argue that in some problems such as binding affinity prediction where molecules are typically presented in a canonical form it might be possible to relax the constraints on permutation invariance of the hypothesis space and learn a more effective model of the affinity by employing an adaptive readout function. Our empirical results demonstrate the effectiveness of neural readouts on more than 40 datasets spanning different domains and graph characteristics. Moreover, we observe a consistent improvement over standard readouts (i.e., sum, max, and mean) relative to the number of neighborhood aggregation iterations and different convolutional operators.

通过神经网络提供自适应readout功能，将节点特征高效地聚合成图级表示，在处理包括图神经网络的许多学习任务中是必不可少的。在某些问题中（如结合亲和力预测），可以松弛假设空间排列不变性的限制并使用自适应readout函数来学习更有效的模型。我们的实证结果证明了神经readout的有效性，相对于不同的卷积运算符和聚合迭代次数，与标准readout（即总和，最大和平均）相比，我们观察到一致的改进。

自适应读取的图神经网络