论文标题
GOGNN：用于预测结构化实体相互作用的图表神经网络图
GoGNN: Graph of Graphs Neural Network for Predicting Structured Entity Interactions
论文作者
论文摘要
实体相互作用预测在许多重要应用中至关重要，例如化学，生物学，材料科学和医学。当每个实体由复杂的结构（即结构化实体）表示时，问题变得非常具有挑战性，因为涉及两种类型的图：结构化实体的本地图和一个全局图来捕获结构化实体之间的相互作用。我们观察到，在结构化实体交互预测上的现有作品无法正确利用图形模型的唯一图。在本文中，我们提出了一个图形神经网络的图，即gognn，该图以层次结构方式提取结构化实体图和实体相互作用图中的特征。我们还提出了双重注意机制，该机制使模型能够在两个级别的图中保留邻居的重要性。对现实世界数据集的广泛实验表明，GOGNN在两个代表性结构化实体相互作用预测任务上的最新方法优于最新方法：化学化学相互作用预测和药物毒品相互作用预测。我们的代码可在GitHub上找到。
Entity interaction prediction is essential in many important applications such as chemistry, biology, material science, and medical science. The problem becomes quite challenging when each entity is represented by a complex structure, namely structured entity, because two types of graphs are involved: local graphs for structured entities and a global graph to capture the interactions between structured entities. We observe that existing works on structured entity interaction prediction cannot properly exploit the unique graph of graphs model. In this paper, we propose a Graph of Graphs Neural Network, namely GoGNN, which extracts the features in both structured entity graphs and the entity interaction graph in a hierarchical way. We also propose the dual-attention mechanism that enables the model to preserve the neighbor importance in both levels of graphs. Extensive experiments on real-world datasets show that GoGNN outperforms the state-of-the-art methods on two representative structured entity interaction prediction tasks: chemical-chemical interaction prediction and drug-drug interaction prediction. Our code is available at Github.
