电子学报 ›› 2022, Vol. 50 ›› Issue (8): 1925-1936.DOI: 10.12263/DZXB.20201436
王飞扬, 冀鹏欣, 孙笠, 危倩, 李根, 张忠宝
收稿日期:
2020-12-15
修回日期:
2021-04-11
出版日期:
2022-08-25
通讯作者:
作者简介:
基金资助:
WANG Fei-yang, JI Peng-xin, SUN Li, WEI Qian, LI Gen, ZHANG Zhong-bao
Received:
2020-12-15
Revised:
2021-04-11
Online:
2022-08-25
Published:
2022-09-08
Corresponding author:
摘要:
社交网络对齐旨在从不同的社交网络中识别出属于同一自然人的社交账户.现有的相关研究大多着眼于静态社交网络的对齐上,然而,社交网络是动态发展的.本文观察到,这种动态性可以揭示出更多的决定性模式,从而更有利于社交网络的对齐,这种现象促使本文在动态场景中重新思考这个问题.于是,本文利用社交网络的动态性,设计一个深度学习架构来解决动态社交网络的对齐问题,其称为DeepDSA(Deep learning based Dynamic Social network Alignment method).首先设计一个深度序列模型来分别捕捉社交网络结构和属性的动态性;其次,对于每一个社交网络,通过保持相同用户结构和属性之间的相关性来融合二元动态,得到原始的综合嵌入表示;最后,以半监督的方式进行空间变换学习,并将每个网络的原始嵌入投影到一个目标子空间中,在该子空间中自然人是唯一表示的.本文在真实世界的数据集上进行大量的实验,证明DeepDSA方法相较于目前的主流算法提升了10%的对齐效果.
中图分类号:
王飞扬, 冀鹏欣, 孙笠, 危倩, 李根, 张忠宝. 一种基于深度学习的动态社交网络用户对齐方法[J]. 电子学报, 2022, 50(8): 1925-1936.
WANG Fei-yang, JI Peng-xin, SUN Li, WEI Qian, LI Gen, ZHANG Zhong-bao. A Deep Learning Based Dynamic Social Network Alignment Method[J]. Acta Electronica Sinica, 2022, 50(8): 1925-1936.
符号 | 定义 |
---|---|
动态社交网络 | |
用户集合 | |
结构特征集合 | |
属性特征集合 | |
已知对齐结点集合 | |
嵌入矩阵 | |
空间映射矩阵 | |
指示矩阵 | |
负指示矩阵 | |
模型参数 |
表1 主要符号和定义
符号 | 定义 |
---|---|
动态社交网络 | |
用户集合 | |
结构特征集合 | |
属性特征集合 | |
已知对齐结点集合 | |
嵌入矩阵 | |
空间映射矩阵 | |
指示矩阵 | |
负指示矩阵 | |
模型参数 |
数据集 | 节点 | 链接 | 文本 | 对齐数 |
---|---|---|---|---|
7 440 | 72 658 | 3 467 472 | 3 896 | |
Foursquare | 6 771 | 69 350 | 289 674 | |
Online | 34 737 | 1 294 814 | 155 296 | 33 158 |
Offline | 34 076 | 1 228 485 | 101 648 |
表2 数据集统计信息
数据集 | 节点 | 链接 | 文本 | 对齐数 |
---|---|---|---|---|
7 440 | 72 658 | 3 467 472 | 3 896 | |
Foursquare | 6 771 | 69 350 | 289 674 | |
Online | 34 737 | 1 294 814 | 155 296 | 33 158 |
Offline | 34 076 | 1 228 485 | 101 648 |
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