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Learning embeddings of a heterogeneous behavior network for potential behavior prediction

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Abstract

Potential behavior prediction involves understanding the latent human behavior of specific groups, and can assist organizations in making strategic decisions. Progress in information technology has made it possible to acquire more and more data about human behavior. In this paper, we examine behavior data obtained in real-world scenarios as an information network composed of two types of objects (humans and actions) associated with various attributes and three types of relationships (human-human, human-action, and action-action), which we call the heterogeneous behavior network (HBN). To exploit the abundance and heterogeneity of the HBN, we propose a novel network embedding method, human-action-attribute-aware heterogeneous network embedding (a4HNE), which jointly considers structural proximity, attribute resemblance, and heterogeneity fusion. Experiments on two real-world datasets show that this approach outperforms other similar methods on various heterogeneous information network mining tasks for potential behavior prediction.

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Authors and Affiliations

  1. College of Computer Science and Technology, Zhejiang University, Hangzhou, 310027, China

    Yue-yang Wang & Yue-ting Zhuang

  2. School of Big Data & Software Engineering, Chongqing University, Chongqing, 401331, China

    Yue-yang Wang

  3. Hikvision Research Institute, Hangzhou Hikvision Digital Technology Co., Ltd., Hangzhou, 310051, China

    Wei-hao Jiang & Shi-liang Pu

Authors
  1. Yue-yang Wang
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  2. Wei-hao Jiang
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  3. Shi-liang Pu
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  4. Yue-ting Zhuang
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Correspondence to Yue-yang Wang.

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Compliance with ethics guidelines

Yue-yang WANG, Wei-hao JIANG, Shi-liang PU, and Yue-ting ZHUANG declare that they have no conflict of interest.

Project supported by the National Natural Science Foundation of China (Nos. U1509206, 61625107, and U1611461) and the Key Program of Zhejiang Province, China (No. 2015C01027)

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Wang, Yy., Jiang, Wh., Pu, Sl. et al. Learning embeddings of a heterogeneous behavior network for potential behavior prediction. Front Inform Technol Electron Eng 21, 422–435 (2020). https://doi.org/10.1631/FITEE.1800493

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  • DOI: https://doi.org/10.1631/FITEE.1800493

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