Graph machine learning models have been successfully deployed in a variety of application areas. One of the most prominent types of models - Graph Neural Networks (GNNs) - provides an elegant way of extracting expressive node-level representation vectors, which can be used to solve node-related problems, such as classifying users in a social network. However, many tasks require representations at the level of the whole graph, e.g., molecular applications. In order to convert node-level representations into a graph-level vector, a so-called readout function must be applied. In this work, we study existing readout methods, including simple non-trainable ones, as well as complex, parametrized models. We introduce a concept of ensemble-based readout functions that combine either representations or predictions. Our experiments show that such ensembles allow for better performance than simple single readouts or similar performance as the complex, parametrized ones, but at a fraction of the model complexity.

翻译：图机器学习模型已成功应用于多种领域。其中最突出的模型类型之一——图神经网络（Graph Neural Networks, GNNs）——提供了一种优雅的方式来提取表达性强的节点级表示向量，可用于解决与节点相关的问题，例如对社会网络中的用户进行分类。然而，许多任务需要整个图级别的表示，例如分子应用。为了将节点级表示转换为图级向量，必须应用所谓的读出函数。在这项工作中，我们研究了现有的读出方法，包括简单的不可训练方法以及复杂的参数化模型。我们引入了基于集成的读出函数概念，该函数结合了表示或预测。我们的实验表明，这种集成方法能够实现比简单单一读出函数更好的性能，或在模型复杂度大幅降低的情况下达到与复杂参数化模型相当的性能。