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CLC number: TP39

On-line Access: 2020-03-18

Received: 2018-08-15

Revision Accepted: 2019-01-31

Crosschecked: 2019-09-18

Cited: 0

Clicked: 5359

Citations:  Bibtex RefMan EndNote GB/T7714

 ORCID:

Yue-yang Wang

http://orcid.org/0000-0003-3210-0930

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Frontiers of Information Technology & Electronic Engineering  2020 Vol.21 No.3 P.422-435

http://doi.org/10.1631/FITEE.1800493


Learning embeddings of a heterogeneous behavior network for potential behavior prediction


Author(s):  Yue-yang Wang, Wei-hao Jiang, Shi-liang Pu, Yue-ting Zhuang

Affiliation(s):  College of Computer Science and Technology, Zhejiang University, Hangzhou 310027, China; more

Corresponding email(s):   yueyangw@zju.edu.cn, jiangweihao5@hikvision.com, pushiliang@hikvision.com, yzhuang@zju.edu.cn

Key Words:  Network embedding, Representation learning, Human behavior, Social networks, Heterogeneous information network, Attribute


Yue-yang Wang, Wei-hao Jiang, Shi-liang Pu, Yue-ting Zhuang. Learning embeddings of a heterogeneous behavior network for potential behavior prediction[J]. Frontiers of Information Technology & Electronic Engineering, 2020, 21(3): 422-435.

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journal="Frontiers of Information Technology & Electronic Engineering",
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pages="422-435",
year="2020",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.1800493"
}

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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.

面向潜在行为预测的异构行为网络嵌入学习

王悦阳1,2,姜伟浩3,蒲世亮3,庄越挺1
1浙江大学计算机科学与技术学院,中国杭州市,310027
2重庆大学大数据与软件学院,中国重庆市,401331
3海康威视研究院,中国杭州市,310051

摘要:潜在行为预测即理解特定群体潜在的人类行为,可辅助组织做出战略决策。信息技术的进步使获取人类行为的庞大数据成为可能。本文将真实场景中获取的人类行为数据构建成信息网络;该信息网络由2种对象(人和动作)和3种关系(人–人、人–动作和动作–动作)组成,称作异构行为网络(HBN)。为充分利用异构行为网络的丰富性和异构性,提出一种网络嵌入方法,称作人–行为–属性感知的异构网络嵌入(a4HNE);该方法综合考虑网络结构邻近性、节点属性相似性和异构性融合。在两个真实数据集上的实验结果表明,该方法在各种异构信息网络挖掘任务中的潜在行为预测性能优于其他同类方法。

关键词:网络嵌入;表示学习;人类行为;社交网络;异构信息网络;属性

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article

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