@inproceedings{16fb728cf3064805beccd65c029342a7,
title = "HetespaceyWalk: A heterogeneous Spacey random walk for heterogeneous information network embedding",
abstract = "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.",
keywords = "Heterogeneous networks, Network embedding, Random walk",
author = "Yu He and Yangqiu Song and Jianxin Li and Cheng Ji and Jian Peng and Hao Peng",
note = "Publisher Copyright: {\textcopyright} 2019 Association for Computing Machinery.; 28th ACM International Conference on Information and Knowledge Management, CIKM 2019 ; Conference date: 03-11-2019 Through 07-11-2019",
year = "2019",
month = nov,
day = "3",
doi = "10.1145/3357384.3358061",
language = "English (US)",
series = "International Conference on Information and Knowledge Management, Proceedings",
publisher = "Association for Computing Machinery",
pages = "639--648",
booktitle = "CIKM 2019 - Proceedings of the 28th ACM International Conference on Information and Knowledge Management",
address = "United States",
}