HeteSpaceyWalk swMATH ID: 39745 Software Authors: Yu He, Yangqiu Song, Jianxin Li, Cheng Ji, Jian Peng, Hao Peng Description: HeteSpaceyWalk: A Heterogeneous Spacey Random Walk for Heterogeneous Information Network Embedding. 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. Homepage: https://arxiv.org/abs/1909.03228 Source Code: https://github.com/HKUST-KnowComp/HeteSpaceyWalk Keywords: Machine Learning; arXiv_cs.LG; Social and Information Networks; arXiv_cs.SI; arXiv_stat.ML; HIN Related Software: word2vec; DeepWalk; LINE; Walklets; node2vec; GraRep Cited in: 1 Document all top 5 Cited by 7 Authors 1 He, Yu 1 Ji, Cheng 1 Li, Jianxin 1 Peng, Fanzhang 1 Peng, Hao 1 Song, Yangqiu 1 Zhang, Xinmiao Cited in 1 Serial 1 The Journal of Artificial Intelligence Research (JAIR) Cited in 2 Fields 1 Probability theory and stochastic processes (60-XX) 1 Computer science (68-XX) Citations by Year