  Recently, graph neural networks have shown the superiority of modeling the
complex topological structures in heterogeneous network-based recommender
systems. Due to the diverse interactions among nodes and abundant semantics
emerging from diverse types of nodes and edges, there is a bursting research
interest in learning expressive node representations in multiplex heterogeneous
networks. One of the most important tasks in recommender systems is to predict
the potential connection between two nodes under a specific edge type (i.e.,
relationship). Although existing studies utilize explicit metapaths to
aggregate neighbors, practically they only consider intra-relationship
metapaths and thus fail to leverage the potential uplift by inter-relationship
information. Moreover, it is not always straightforward to exploit
inter-relationship metapaths comprehensively under diverse relationships,
especially with the increasing number of node and edge types. In addition,
contributions of different relationships between two nodes are difficult to
measure. To address the challenges, we propose HybridGNN, an end-to-end GNN
model with hybrid aggregation flows and hierarchical attentions to fully
utilize the heterogeneity in the multiplex scenarios. Specifically, HybridGNN
applies a randomized inter-relationship exploration module to exploit the
multiplexity property among different relationships. Then, our model leverages
hybrid aggregation flows under intra-relationship metapaths and randomized
exploration to learn the rich semantics. To explore the importance of different
aggregation flow and take advantage of the multiplexity property, we bring
forward a novel hierarchical attention module which leverages both
metapath-level attention and relationship-level attention. Extensive
experimental results suggest that HybridGNN achieves the best performance
compared to several state-of-the-art baselines.
