基于深度学习的跨社交网络用户匹配方法

马强; 戴军

doi:10.11999/JEIT220702

基于深度学习的跨社交网络用户匹配方法

doi: 10.11999/JEIT220702

马强,
戴军^,

西南科技大学信息工程学院绵阳 621010

基金项目: 国家自然科学基金 (62071170, 62072158)，河南省杰出青年科学基金(222300420006)，河南省高校科技创新团队支持计划(21IRTSTHN015)，西南科技大学博士基金(17zx7158)

详细信息

作者简介:
马强：男，副教授，研究方向为智能数据处理、社交媒体计算

戴军：男，硕士生，研究方向为跨社交媒体计算、用户账号匹配

通讯作者:
戴军　arturia_pendragon@163.com

中图分类号: TN915; TP391
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- 文章访问数: 581
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- 被引次数: 0
出版历程
- 收稿日期: 2022-05-31
- 修回日期: 2022-08-27
- 录用日期: 2022-09-06
- 网络出版日期: 2022-09-09
- 刊出日期: 2023-07-10

User Matching Method for Cross Social Networks Based on Deep Learning

MA Qiang,
DAI Jun^,

School of Information Engineering, Southwest University of Science and Technology, Mianyang 621010, China

Funds: The National Natural Science Foundation of China (62071170，62072158), Henan Science Foundation for Distinguished Young Scholars (222300420006), Henan Support Plan for Science and Technology Innovation Team of Universities (21IRTSTHN015), The Doctoral Foundation of Southwest University of Science and Technology (17zx7158)

摘要

摘要: 现有基于时空信息的跨社交网络用户匹配方案，存在着难以耦合时空信息、特征提取困难问题，导致匹配精度下降。该文提出一种基于深度学习的跨社交网络用户匹配方法(DLUMCN)，首先对用户签到数据进行时空尺度的网格映射，生成包含用户特征的签到矩阵集合，对其归一化后构成用户签到图。然后采用卷积从签到图中生成高维度的时空特征图，利用深度可分离卷积对特征图权重变换和特征融合，对特征图1维展开获得特征向量。最后利用全连接前馈网络构建分类器并输出用户匹配评分。通过在两组真实社交网络的数据集上进行实验验证，实验结果表明，与现有相关算法相比，所提算法在匹配的准确率以及F1-值均得到提升，验证了所提算法的有效性。
- 跨社交网络 /
- 用户匹配 /
- 深度学习 /
- 签到相似度
Abstract: The existing spatio-temporal information based user matching schemes for cross social networks have problems of spatio-temporal information decoupling and feature extraction difficulties, which result in a decrease in matching accuracy. A Deep Learning based User Matching method for Cross social Networks (DLUMCN) is proposed. Firstly, grid mapping at the spatio-temporal scale is carried out on the user sign-in data. The sign-in matrix set is generated, which contains user characteristics. User sign-in map is formed after normalization. Secondly, the convolution is used to generate high-dimensional spatio-temporal feature maps from the user sign-in map. The weight transformation and feature fusion of feature maps are carried out by deep separable convolution. The feature vector is obtained by one-dimensional expansion of feature maps. Finally, the fully connected feedforward network is used to build a classifier and output the user matching score. Experimental results on two sets of datasets of real social networks show that the proposed method has improved matching accuracy and F1-value, compared with the existing related methods. The effectiveness of the proposed method is demonstrated.
- Cross social networks /
- User matching /
- Deep learning /
- Sign-in similarity

HTML全文

图 1 匹配模型

下载: 全尺寸图片幻灯片

图 2 模型的准确率和损失曲线

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图 3 模型在不同条件下的准确率

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图 4 关联系数s和填充系数p的确定

下载: 全尺寸图片幻灯片

算法1　单点填充算法
输入：用户签到集S，与S待匹配签到集S_match，网格密度系数k
输出：用户签到矩阵集SMS
初始化：初始化k维的零矩阵集SMS={A_s1, A_s2, A_t1, A_t2}，通　过S和S_match设定全局时空域
(1) 遍历签到集S：
(2) 获取时空网格：g_s=(x_s, y_s)，g_t=(x_t, y_t)
(3) 链接并填充矩阵：A_s1[x_s, y_s] += 1；A_t1[x_t, y_t] += 1
(4) 通过S和S_match设定局部时空域，将A_s1和A_t1替换为A_s2和A_t2，　重复执行步骤1～步骤3
(5) 输出SMS

下载: 导出CSV

算法2　关联填充算法
输入：用户签到集S，与S待匹配签到集S_match，网格密度系数k，　关联系数s，填充系数p
输出：用户签到矩阵集SMS
(1) 通过单点填充算法获得SMS={A_s1, A_s2, A_t1, A_t2}，定义空　集合A, B
(2) 通过S和S_match设定全局时空域，选定A_s = A_s1 A_t = A_t1,
(3) 遍历签到集S中任意签到sign：
(4) 获取sign的时空网格表示g_s=(x_s, y_s)和g_t=(x_t, y_t)
(5) 将满足d(g, g_s) <= s的网格g，将其添加到临时集合A_t
(6) 将满足d(g, g_t) <= s的网格g，将其添加到临时集合B_t
(7) 对任意网格g=(x, y)：
(8) 若g ∈ A_t∩A：A_s[x, y]+=p
(9) 若g ∈ B_t∩B：A_t[x, y]+=p
(10) 更新集合A和B：A=A_t，B=B_t
(11) 若当前映射空间为全局时空域：通过S和S_match设定局部时　空域，选定A_s = A_s2 A_t = A_t2，重复执行步骤3～步骤10
(12) 否则：输出SMS

下载: 导出CSV

算法3　模型优化算法
输入：训练样本集train_data，迭代轮数epoch，批次尺寸bs，　学习率α
输出：model
(1) 随机初始化model{W_C1, ···, W_F1, W_OUT, B_C1, ···, B_F1, b}
(2) for i =1 to epoch:
(3) 　　for batch_data in train_data: #按批次遍历整个训练　　　　样本集
(4) 　　　　for sample in batch_data: #按样本遍历单个批次
(5) 　　　　　构建样本签到图MAP
(6) 　　　　　根据式(12)进行前向传播，计算模型各层输出
(7) 　　　根据式(13)—式(16)计算模型预测的损失和各层参数　　　　　的梯度
(8) 　　　更新模型各层参数：W–= α×δW，B–= α×δB
(9) 输出 model

下载: 导出CSV

表 1 数据集概况

	数据集
	Brightkite(样本量50686)		Gowalla(样本量107092)
划分部分	a	b	a	b
平均签到数	111	111	75	75
起始时间	2008–03–22 06:34:37	2008–03–21 20:36:21	2009–02–05 06:27:43	2009–02–04 05:17:38
终止时间	2010–10–18 18:34:01	2010–10–18 18:39:58	2010–10–23 05:22:06	2010–10–23 05:22:06
经度范围	–163.193～151.198	–163.193～151.198	–90.011～105.659	–90.011～105.625
纬度范围	–179.824～179.999	–179.824～179.999	–176.309～177.463	–166.525～177.453

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表 2 匹配模型的其他参数

模型参数	设定值	说明
k	65(Brightkite) / 75(Gowalla)	网格密度系数
det/mt	1×10^–4/0.5	调节因子/匹配阈值
batch-size，α	256, 1×10^–3	训练的批次样本量，学习率
kcc1(fc1), kcc2(fc2)	16(3), 32(3)	CON1, CON2卷积核数 (尺寸)
kcs1(fs1), kcs2(fs2)	1(2), 64(1)	SCON1, SCON2卷积核数(尺寸)
n_F1, n_F2, n_F3	16, 12, 8	前馈网络隐藏层神经元数量

下载: 导出CSV

表 3 不同算法的复杂度以及耗时(s)

算法	时间复杂度	空间复杂度	(Brightkite数据集)		(Gowalla数据集)
算法	时间复杂度	空间复杂度	训练时间	测试时间	训练时间	测试时间
UNICORN	O(N²·S·k²)	O(N·S)	–	11.85	–	25.12
STUL	O(N·S³)	O(N·S)	–	451.663	–	1394.156
CDTraj2vec	O(N·S²)	O(N·S²)	375.833	11.85	587.174	26.385
UIDwST	O(N²·S²)	O (N·S)	–	217.775	–	631.105
DLUMCN	O(N·S·k²)	O(N·k²)	69.678	0.475	200.066	1.311

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表 4 不同算法在两组数据集上的测试结果

算法	(Brightkite数据集)				(Gowalla数据集)
算法	acc	pre	rec	f1	acc	pre	rec	f1
UNICORN	0.8801	0.8085	0.9960	0.8925	0.8847	0.8194	0.9870	0.8954
STUL	0.9253	0.9039	0.9516	0.9271	0.9281	0.9094	0.9447	0.9267
CDTraj2vec	0.9529	0.9833	0.9212	0.9512	0.9567	0.9686	0.9360	0.9520
UIDwST	0.9740	0.9594	0.9897	0.9743	0.9773	0.9619	0.9870	0.9742
DLUMCN	0.9881	0.9949	0.9814	0.9881	0.9890	0.9969	0.9812	0.9889

下载: 导出CSV

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