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应用领域。其中最突出的模型类型之一——图神经网络（GNN）——提供了一种优雅的方法来提取具有表达力的节点级表示向量，这些向量可用于解决与节点相关的问题，例如对社交网络中的用户进行分类。然而，许多任务需要全图级别的表示，例如分子应用领域。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。在本工作中，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。我们引入了一种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，此类集成方法能够比简单的单一读出函数实现更好的性能，或达到与复杂参数化模型相当的性能，但模型复杂度仅为后者的几分之一。