基于可拓展自注意力时空图卷积神经网络的用户轨迹识别模型

雷天亮; 吉立新; 王庚润; 刘树新; 巫岚

doi:10.12263/DZXB.20221225

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基于可拓展自注意力时空图卷积神经网络的用户轨迹识别模型

学术论文 | 更新时间：2025-12-11

- 基于可拓展自注意力时空图卷积神经网络的用户轨迹识别模型
- User Trajectory Identification Based on Expandable Self-Attention Spatio-Temporal Graph Convolutional Neural Networks
- 电子学报 2024年52卷第11期页码：3741-3750
- 作者机构：
  
  信息工程大学信息技术研究所，河南郑州 450002
- 作者简介：
  
  [ "雷天亮男，1998年出生，安徽宣城人.现为信息工程大学硕士研究生.主要研究方向为用户身份识别.E-mail: leitianliang1@163.com" ]
  [ "吉立新男，1970年出生，江苏淮安人.现为国家数字交换系统工程技术研究中心研究员，博士生导师.主要研究方向为数据挖掘、电信网安全.E-mail: jlxndsc@139.com" ]
  [ "王庚润男，1987年出生，安徽蒙城人.现为国家数字交换系统工程技术研究中心副研究员.主要研究方向为电信网安全、数据处理." ]
  [ "刘树新男，1987年出生，山东潍坊人.现为国家数字交换系统工程技术研究中心助理研究员.主要研究方向为链路预测、通信网络安全." ]
  [ "巫岚女，1994年出生，四川遂宁人.现为国家数字交换系统工程技术研究中心研究实习员.主要研究方向为行为分析和网络空间安全." ]
- 基金信息：
  
  河南省重大科技专项项目(221100210700)
- DOI：10.12263/DZXB.20221225
  中图分类号： TP391;
- 收稿：2022-10-28，
  
  修回：2023-03-03，
  
  纸质出版：2024-11-25
- 稿件说明：
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雷天亮, 吉立新, 王庚润, 等. 基于可拓展自注意力时空图卷积神经网络的用户轨迹识别模型[J]. 电子学报, 2024, 52(11): 3741-3750.

LEI Tian-liang, JI Li-xin, WANG Geng-run, et al. User Trajectory Identification Based on Expandable Self-Attention Spatio-Temporal Graph Convolutional Neural Networks[J]. Acta Electronica Sinica, 2024, 52(11): 3741-3750.
雷天亮, 吉立新, 王庚润, 等. 基于可拓展自注意力时空图卷积神经网络的用户轨迹识别模型[J]. 电子学报, 2024, 52(11): 3741-3750. DOI：10.12263/DZXB.20221225

LEI Tian-liang, JI Li-xin, WANG Geng-run, et al. User Trajectory Identification Based on Expandable Self-Attention Spatio-Temporal Graph Convolutional Neural Networks[J]. Acta Electronica Sinica, 2024, 52(11): 3741-3750. DOI：10.12263/DZXB.20221225

摘要

用户轨迹识别作为一项重要的时空数据挖掘任务，广泛应用于基于位置的个性化服务推荐、行程规划、犯罪行为检测和目标跟踪等领域，但依然面临预测精度不高的问题，主要原因是轨迹数据低采样且稀疏、轨迹类别数量巨大等.针对上述问题提出了基于可拓展自注意力时空图卷积神经网络的用户轨迹识别模型（Expandable Self-Attention Spatio-Temporal Graph Convolutional Neural Networks，ESAST-GCNN），该模型采用时空图卷积神经网络方式，深度挖掘时序特征与空间特征关系，并进行预测与拓展，结合自注意力机制获取用户轨迹特征向量内部相关性，最终根据该特征向量进行用户轨迹身份识别.在两个真实数据集上进行测试后发现，ESAST-GCNN相较于TULER-GRU（TUL via Embedding and RNN）在Geolife与Gowalla中准确率分别提高了13.95%、10.63%，实验结果表明ESAST-GCNN优于其他模型，识别效果更好，适用范围更广.

Abstract

As an important spatio-temporal data mining task

user trajectory identification is widely used in the fields of location-based personalized service recommendation

itinerary planning

crime behavior detection

and target tracking.However

it still has low prediction accuracy

mainly due to low sampling and sparse trajectory data

and a huge number of trajectory categories.To fill the research gaps

a user trajectory identification model based on an expandable self-attention spatio-temporal graph convolutional neural network (ESAST-GCNN) is proposed

which adopts the spatio-temporal graph convolutional neural network to deeply mine the relationship between time sequence features and spatial features to predict and expand the sequence.This model combines the self-attention mechanism to obtain the internal correlation of user trajectory feature vectors and identify user trajectories.After testing on two real datasets

the results show that the accuracy of ESAST-GCNN is improved by 13.95% and 10.63% in Geolife and Gowalla compared with TUL via Embedding and RNN (TULER-GRU)

respectively.The experimental results illustrate that ESAST-GCNN is superior to other comparative models

with better identification effect and wider applicability.

关键词

Keywords

references

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