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An Efficient Link Prediction Model in Dynamic Heterogeneous Information Networks Based on Multiple Self-attention

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Knowledge Science, Engineering and Management (KSEM 2021)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 12817))

Abstract

The existing link prediction researches of information networks mainly focus on the dynamic homogeneous network or the static heterogeneous network. It has always been a challenge to predict future relationships between nodes while learning both continuous-time and heterogeneous information simultaneously. In this paper, we propose a Heterogeneous and Continuous-Time Model Based on Self-Attention (HTAT) to complete the link prediction task by learning temporal evolution and heterogeneity jointly. The HTAT model consists of the base layer and the heterogeneous layer. The base layer incorporates a functional time encoding with self-attention mechanism to capture continuous-time evolution. And the heterogeneous layer consists of multi-view attention to learn heterogeneous information. Experimental results show that HTAT is significantly competitive compared with four state-of-the-art baselines on three real-world datasets.

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Notes

  1. 1.

    http://snap.stanford.edu/data/sx-mathoverflow.html.

  2. 2.

    http://snap.stanford.edu/data/sx-superuser.html.

  3. 3.

    https://tianchi.aliyun.com/competition/entrance/231721/information.

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Correspondence to Beibei Ruan .

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Ruan, B., Zhu, C. (2021). An Efficient Link Prediction Model in Dynamic Heterogeneous Information Networks Based on Multiple Self-attention. In: Qiu, H., Zhang, C., Fei, Z., Qiu, M., Kung, SY. (eds) Knowledge Science, Engineering and Management. KSEM 2021. Lecture Notes in Computer Science(), vol 12817. Springer, Cham. https://doi.org/10.1007/978-3-030-82153-1_6

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  • DOI: https://doi.org/10.1007/978-3-030-82153-1_6

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  • Online ISBN: 978-3-030-82153-1

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