Graph Neural Networks (GNNs) have been widely used for the representation learning of various structured graph data. While promising, most existing GNNs oversimplified the complexity and diversity of the edges in the graph, and thus inefficient to cope with ubiquitous heterogeneous graphs, which are typically in the form of multi-relational graph representations. In this paper, we propose RioGNN, a novel Reinforced, recursive and flexible neighborhood selection guided multi-relational Graph Neural Network architecture, to navigate complexity of neural network structures whilst maintaining relation-dependent representations. We first construct a multi-relational graph, according to the practical task, to reflect the heterogeneity of nodes, edges, attributes and labels. To avoid the embedding over-assimilation among different types of nodes, we employ a label-aware neural similarity measure to ascertain the most similar neighbors based on node attributes. A reinforced relation-aware neighbor selection mechanism is developed to choose the most similar neighbors of a targeting node within a relation before aggregating all neighborhood information from different relations to obtain the eventual node embedding. Particularly, to improve the efficiency of neighbor selecting, we propose a new recursive and scalable reinforcement learning framework with estimable depth and width for different scales of multi-relational graphs. RioGNN can learn more discriminative node embedding with enhanced explainability due to the recognition of individual importance of each relation via the filtering threshold mechanism. Comprehensive experiments on real-world graph data and practical tasks demonstrate the advancements of effectiveness, efficiency and the model explainability, as opposed to other comparative GNN models.
翻译:图像神经网络(Neural Networks) 已被广泛用于各种结构化图形数据的代表学习。 虽然大多数现有的GNNs很有希望,但多数现有的GNS过度简化了图形边缘的复杂性和多样性,从而无法有效地应对无处不在的多变量图形,这些图形通常以多关系图形表示的形式存在。我们在此文件中提议了RioGNN,这是一个新的强化、循环和灵活的社区选择指导多关系图形神经网络结构,用于导航神经网络结构的复杂性,同时保持基于关系的表达方式。我们首先根据实际任务,构建了一个多关系结构图,以反映图表边缘的复杂性和多样性,从而反映结点、边缘、属性和标签的异异异异性图形的异性。为了避免不同类型结点之间的超异性,我们使用一个标签-觉神经相似性测量测量尺度来根据偏近的属性确定最相似的邻居。一个强化关系-了解邻居选择一个最相似的结点的邻居,在关系中选择一个最相似的邻居,然后根据实际任务,然后根据具体的任务,根据具体的任务,然后将不同关系汇总所有邻居信息合并,反映节点、边缘、边缘、边缘、边缘、边缘、边缘、边缘的临界关系异性标准,解释各种关系的异性比、解释,从而选择更高级的升级的模型,从而选择一个更高级的模型,从而选择更高级的升级的升级的升级的升级的升级的升级的升级的升级的模型。