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.

本文提出了一种异构个性化空间随机游走算法，将元路径引导的随机游走过程形式化为高阶马尔可夫链进而解决马尔可夫链的稳定性问题，实现了多类型节点的嵌入学习，并在多种异构网络上进行了实验验证。