  Recent research on network embedding in hyperbolic space have proven
successful in several applications. However, nodes in real world networks tend
to interact through several distinct channels. Simple aggregation or ignorance
of this multiplexity will lead to misleading results. On the other hand, there
exists redundant information between different interaction patterns between
nodes. Recent research reveals the analogy between the community structure and
the hyperbolic coordinate. To learn each node's effective embedding
representation while reducing the redundancy of multiplex network, we then
propose a unified framework combing multiplex network hyperbolic embedding and
multiplex community detection. The intuitive rationale is that high order node
embedding approach is expected to alleviate the observed network's sparse and
noisy structure which will benefit the community detection task. On the
contrary, the improved community structure will also guide the node embedding
task. To incorporate the common features between channels while preserving
unique features, a random walk approach which traversing in latent multiplex
hyperbolic space is proposed to detect the community across channels and bridge
the connection between node embedding and community detection. The proposed
framework is evaluated on several network tasks using different real world
dataset. The results demonstrates that our framework is effective and
efficiency compared with state-of-the-art approaches.
