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Semantic-aware heterogeneous information network embedding with incompatible meta-paths

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Abstract

Heterogeneous information network (HIN) embedding represents heterogeneous nodes as vectors in the low-dimensional space. Meta-path is used to measure the nodes similarity to guide HIN embedding. Existing works assume that different meta-paths share the same semantic space and directly fuse the different mate-paths for node similarity calculation. This ignores the incompatibility of different meta-paths, which cannot reflect the real relationship between nodes. To solve the problems of existing works, a novel S emantic-A ware H IN E mbedding (SAHE) is proposed to fuse incompatible meta-paths for node similarity measurement. The key idea of the proposed method is to measure the relative similarity relationship on each meta-path in its own semantic space, and aggregate these similarity relationships to obtain the node similarity to calculate HIN embedding. The kendall tau distance is used to aggregate the different similarity relationship in multiple semantic spaces. The semantic preference is extracted as a constraint to optimize the aggregated similarity matrix. The Kullback-Leibler Divergence (KL Divergence) is used to learn nodes embedding by measuring the node similarity distribution in the embedded space. Experiments on three real HIN datasets verify that the superiority of the proposed model is superior to other state-of-the-art methods on the node classification and the node clustering tasks.

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Acknowledgements

This research is supported by National Natural Science Foundation of China (No. 61672284), Natural Science Foundation of Jiangsu Province (No. BK20171418), Postdoctoral Science Foundation of China (No. 2016M591841) and Jiangsu Planned Projects for Postdoctoral Research Funds (No. 1601225C).

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  1. Department of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, China

    Susu Zheng, Donghai Guan & Weiwei Yuan

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  1. Susu Zheng
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  2. Donghai Guan
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Correspondence to Weiwei Yuan.

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Zheng, S., Guan, D. & Yuan, W. Semantic-aware heterogeneous information network embedding with incompatible meta-paths. World Wide Web 25, 1–21 (2022). https://doi.org/10.1007/s11280-021-00903-5

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