采用用户名相似度传播模型的线上用户身份属性关联方法

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采用用户名相似度传播模型的线上用户身份属性关联方法

更新时间：2025-07-09

- 采用用户名相似度传播模型的线上用户身份属性关联方法
- Issue 4, Pages: 1-6+27(2016)
- 作者机构：
  
  1. 西安交通大学智能网络与网络安全教育部重点实验室
  2. 清华大学智能与网络化系统研究中心
  3. 西北工业大学自动化学院
- 作者简介：
- 基金信息：
- DOI：
  CLC： TP309
- Published：2016
- 稿件说明：
移动端阅览
[J]. 2016, (4): 1-6+27.
DOI：

[J]. 2016, (4): 1-6+27. DOI：

摘要

针对用户跨线上行为复杂多样难以融合监控的问题

提出了基于用户名相似度传播模型的线上用户身份属性关联方法。结合中文社交网络中用户名的特征

将用户名中的中英文字符进行分离

并采用贪婪算法分别求取不同用户名之间的中英文字符串的最大公共子串

以此实现含中英文字符的用户名相似度的计算;结合用户线上的好友结构网络

仅利用一阶邻居的用户名相似度求解用户对的匹配度

由此不但实现了用户名相似度沿网络结构的快速传播

也大幅度地降低了匹配算法的计算复杂度。结合所收集的新浪微博和人人网中用户身份属性数据的实验结果表明:新提出的字符串匹配算法将用户名匹配准确率提升了近30%

传播模型也大幅度地减少了用户名匹配的计算量

分析结果不但可以实现用户跨线上应用行为的关联融合

也对网络舆论控制和行为监管具有重要的参考价值。

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