  Heterogeneous Information Network (HIN) embedding refers to the
low-dimensional projections of the HIN nodes that preserve the HIN structure
and semantics. HIN embedding has emerged as a promising research field for
network analysis as it enables downstream tasks such as clustering and node
classification. In this work, we propose \ours for joint learning of cluster
embeddings as well as cluster-aware HIN embedding. We assume that the connected
nodes are highly likely to fall in the same cluster, and adopt a variational
approach to preserve the information in the pairwise relations in a
cluster-aware manner. In addition, we deploy contrastive modules to
simultaneously utilize the information in multiple meta-paths, thereby
alleviating the meta-path selection problem - a challenge faced by many of the
famous HIN embedding approaches. The HIN embedding, thus learned, not only
improves the clustering performance but also preserves pairwise proximity as
well as the high-order HIN structure. We show the effectiveness of our approach
by comparing it with many competitive baselines on three real-world datasets on
clustering and downstream node classification.
