Learning embeddings of a heterogeneous behavior network for potential behavior prediction

WANG Yue-yang; JIANG Wei-hao; PU Shi-liang; ZHUANG Yue-ting

doi:10.1631/FITEE.1800493

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Learning embeddings of a heterogeneous behavior network for potential behavior prediction

Regular Papers | Updated：2022-05-19

- Learning embeddings of a heterogeneous behavior network for potential behavior prediction
- 面向潜在行为预测的异构行为网络嵌入学习
- Frontiers of Information Technology & Electronic Engineering Vol. 21, Issue 3, Pages: 422-435(2020)
- Affiliations：
  
  College of Computer Science and Technology, Zhejiang University, Hangzhou 310027, China
  School of Big Data & Software Engineering, Chongqing University, Chongqing 401331, China
  Hikvision Research Institute, Hangzhou Hikvision Digital Technology Co., Ltd., Hangzhou 310051, China
- Author bio：
  
  Yue-yang WANG, yueyangw@zju.edu.cn
  [ "Wei-hao JIANG, E-mail:jiangweihao5@hikvision.com" ]
  [ "Shi-liang PU, E-mail:pushiliang@hikvision.com" ]
  [ "Yue-ting ZHUANG, E-mail:yzhuang@zju.edu.cn" ]
- Funds：
- DOI：10.1631/FITEE.1800493
  CLC： TP39
- Received：15 August 2018，
  
  Revised：2019-;9-18，
  
  Published Online：27 December 2019，
  
  Published：2020-03
- Accepted：
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WANG Yue-yang, JIANG Wei-hao, PU Shi-liang, et al. Learning embeddings of a heterogeneous behavior network for potential behavior prediction[J]. Frontiers of Information Technology & Electronic Engineering, 2020, 21(3): 422-435.
DOI：

WANG Yue-yang, JIANG Wei-hao, PU Shi-liang, et al. Learning embeddings of a heterogeneous behavior network for potential behavior prediction[J]. Frontiers of Information Technology & Electronic Engineering, 2020, 21(3): 422-435. DOI： 10.1631/FITEE.1800493.

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（a

HNE）；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。

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 realworld 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 (a

HNE)

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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