HeteSpaceyWalk: A Heterogeneous Spacey Random Walk for Heterogeneous Information Network Embedding
This work addresses the stationarity issue in random-walk based embeddings for heterogeneous networks, which is incremental but improves accuracy for tasks like node classification and link prediction.
The paper tackled the problem of heterogeneous information network embedding by formalizing meta-path guided random walks as higher-order Markov chains and introducing a heterogeneous personalized spacey random walk to achieve stationary distributions, resulting in substantial performance improvements over state-of-the-art methods in experiments on several networks.
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.