  Graph Convolutional Neural Networks (GCNs) have become effective machine
learning algorithms for many downstream network mining tasks such as node
classification, link prediction, and community detection. However, most GCN
methods have been developed for homogenous networks and are limited to a single
embedding for each node. Complex systems, often represented by heterogeneous,
multiplex networks present a more difficult challenge for GCN models and
require that such techniques capture the diverse contexts and assorted
interactions that occur between nodes. In this work, we propose RAHMeN, a novel
unified relation-aware embedding framework for attributed heterogeneous
multiplex networks. Our model incorporates node attributes, motif-based
features, relation-based GCN approaches, and relational self-attention to learn
embeddings of nodes with respect to the various relations in a heterogeneous,
multiplex network. In contrast to prior work, RAHMeN is a more expressive
embedding framework that embraces the multi-faceted nature of nodes in such
networks, producing a set of multi-embeddings that capture the varied and
diverse contexts of nodes.
  We evaluate our model on four real-world datasets from Amazon, Twitter,
YouTube, and Tissue PPIs in both transductive and inductive settings. Our
results show that RAHMeN consistently outperforms comparable state-of-the-art
network embedding models, and an analysis of RAHMeN's relational self-attention
demonstrates that our model discovers interpretable connections between
relations present in heterogeneous, multiplex networks.
