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

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