  Representation learning of networks has witnessed significant progress in
recent times. Such representations have been effectively used for classic
network-based machine learning tasks like node classification, link prediction,
and network alignment. However, very few methods focus on capturing
representations for multiplex or multilayer networks, which are more accurate
and detailed representations of complex networks. In this work, we propose
Multi-Net a fast and scalable embedding technique for multiplex networks.
Multi-Net, effectively maps nodes to a lower-dimensional space while preserving
its neighborhood properties across all the layers. We utilize four random walk
strategies in our unified network embedding model, thus making our approach
more robust than existing state-of-the-art models. We demonstrate superior
performance of Multi-Net on four real-world datasets from different domains. In
particular, we highlight the uniqueness of Multi-Net by leveraging it for the
complex task of network reconstruction.
