  Graph Neural Networks (GNNs) have been widely used for the representation
learning of various structured graph data, typically through message passing
among nodes by aggregating their neighborhood information via different
operations. 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.
