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.
翻译:通过读取功能将节点特性有效整合成图表层次的表达方式是许多涉及图形神经网络的学习任务的关键步骤。 通常, 读取是简单且非适应性功能, 通常设计成简单且非适应性功能, 从而导致假设空间的变异。 先前的深层组合工作表明, 这些重现可能需要复杂的节点嵌入, 而这种嵌入可能很难通过标准的邻居群集计划来学习 。 以此为动力, 我们调查神经网络提供的适应性读取的潜力, 这些网络不一定导致变异假设空间的变异。 我们认为, 在一些问题中, 比如, 分子通常以粗体形式呈现的结合性预测, 可能会通过使用适应性读取功能, 来放松对假设空间变异的制约, 并学习一个更有效的亲和模式。 我们的经验结果表明, 40多个跨越不同区域和图形特性的数据集的神经读取效果。 此外, 我们观察到, 在标准读取( e. sum, im, 和 mean) 相对于区域运营商数量而言, 我们可能看到不断改进。