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.

该论文提出了一种新的 Graph Neural Network 体系架构（RioGNN），通过使用增强、递归和灵活的邻域选择机制来处理复杂和多样化的边缘，并通过在不同关系之间筛选重要关系来提高效率和解释性。实验结果表明，该方法比其他比较方法更有效，更高效，并具有更好的模型解释性。