  Heterogeneous information network (HIN) embedding has gained increasing
interests recently. However, the current way of random-walk based HIN embedding
methods have paid few attention to the higher-order Markov chain nature of
meta-path guided random walks, especially to the stationarity issue. In this
paper, we systematically formalize the meta-path guided random walk as a
higher-order Markov chain process, and present a heterogeneous personalized
spacey random walk to efficiently and effectively attain the expected
stationary distribution among nodes. Then we propose a generalized scalable
framework to leverage the heterogeneous personalized spacey random walk to
learn embeddings for multiple types of nodes in an HIN guided by a meta-path, a
meta-graph, and a meta-schema respectively. We conduct extensive experiments in
several heterogeneous networks and demonstrate that our methods substantially
outperform the existing state-of-the-art network embedding algorithms.
