Message passing neural networks (MPNNs) operate on graphs by exchanging information between neigbouring nodes. MPNNs have been successfully applied to various node-, edge-, and graph-level tasks in areas like molecular science, computer vision, natural language processing, and combinatorial optimization. However, most MPNNs require training on large amounts of labeled data, which can be costly and time-consuming. In this work, we explore the use of various untrained message passing layers in graph neural networks, i.e. variants of popular message passing architecture where we remove all trainable parameters that are used to transform node features in the message passing step. Focusing on link prediction, we find that untrained message passing layers can lead to competitive and even superior performance compared to fully trained MPNNs, especially in the presence of high-dimensional features. We provide a theoretical analysis of untrained message passing by relating the inner products of features implicitly produced by untrained message passing layers to path-based topological node similarity measures. As such, untrained message passing architectures can be viewed as a highly efficient and interpretable approach to link prediction.

翻译：消息传递神经网络（MPNNs）通过在相邻节点间交换信息来操作图数据。MPNNs已成功应用于分子科学、计算机视觉、自然语言处理和组合优化等领域的各类节点级、边级和图级任务。然而，大多数MPNNs需要大量标注数据进行训练，这通常成本高昂且耗时。本研究探索了在图神经网络中使用多种未训练消息传递层的方法，即对流行的消息传递架构进行变体改造，移除消息传递步骤中用于转换节点特征的所有可训练参数。聚焦于链接预测任务，我们发现未训练消息传递层能够取得与完全训练的MPNNs相竞争甚至更优的性能，尤其是在存在高维特征的情况下。我们通过将未训练消息传递层隐式生成的特征内积与基于路径的拓扑节点相似性度量相关联，提供了未训练消息传递的理论分析。因此，未训练消息传递架构可视为一种高效且可解释的链接预测方法。