论文标题
神经信息传递高级路径
Neural Message Passing on High Order Paths
论文作者
论文摘要
图神经网络在预测分子特性方面取得了令人印象深刻的结果，但它们并未直接考虑图中局部和隐藏结构，例如官能团和分子几何形状。在每个传播步骤中，GNNS仅在一阶邻居上汇总，忽略了后续邻居中包含的重要信息以及这些高阶连接之间的关系。在这项工作中，我们将图形神经网推广到传递消息并跨高阶路径进行聚合。这允许信息在图形的各个级别和子结构上传播。我们在分子属性预测中的一些任务上演示了我们的模型。
Graph neural network have achieved impressive results in predicting molecular properties, but they do not directly account for local and hidden structures in the graph such as functional groups and molecular geometry. At each propagation step, GNNs aggregate only over first order neighbours, ignoring important information contained in subsequent neighbours as well as the relationships between those higher order connections. In this work, we generalize graph neural nets to pass messages and aggregate across higher order paths. This allows for information to propagate over various levels and substructures of the graph. We demonstrate our model on a few tasks in molecular property prediction.
