  In graph neural networks (GNNs), message passing iteratively aggregates
nodes' information from their direct neighbors while neglecting the sequential
nature of multi-hop node connections. Such sequential node connections e.g.,
metapaths, capture critical insights for downstream tasks. Concretely, in
recommender systems (RSs), disregarding these insights leads to inadequate
distillation of collaborative signals. In this paper, we employ collaborative
subgraphs (CSGs) and metapaths to form metapath-aware subgraphs, which
explicitly capture sequential semantics in graph structures. We propose
meta\textbf{P}ath and \textbf{E}ntity-\textbf{A}ware \textbf{G}raph
\textbf{N}eural \textbf{N}etwork (PEAGNN), which trains multilayer GNNs to
perform metapath-aware information aggregation on such subgraphs. This
aggregated information from different metapaths is then fused using attention
mechanism. Finally, PEAGNN gives us the representations for node and subgraph,
which can be used to train MLP for predicting score for target user-item pairs.
To leverage the local structure of CSGs, we present entity-awareness that acts
as a contrastive regularizer on node embedding. Moreover, PEAGNN can be
combined with prominent layers such as GAT, GCN and GraphSage. Our empirical
evaluation shows that our proposed technique outperforms competitive baselines
on several datasets for recommendation tasks. Further analysis demonstrates
that PEAGNN also learns meaningful metapath combinations from a given set of
metapaths.
