基于图卷积网络的社交网络Spammer检测技术

曲强; 于洪涛; 黄瑞阳

doi:10.11959/j.issn.2096-109x.2018042

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基于图卷积网络的社交网络Spammer检测技术

学术论文 | 更新时间：2024-06-03

- 基于图卷积网络的社交网络Spammer检测技术
- Spammer detection technology of social network based on graph convolution network
- 网络与信息安全学报 2018年4卷第5期页码：39-46
- 作者机构：
- 作者简介：
  
  [ "曲强（1994-），男，黑龙江齐齐哈尔人，国家数字交换系统工程技术研究中心硕士生，主要研究方向为网络空间安全、大数据分析与处理、复杂网络异常用户检测。" ]
  [ "于洪涛（1970-），男，辽宁丹东人，博士，国家数字交换系统工程技术研究中心研究员，主要研究方向为网络大数据分析与处理。" ]
  [ "黄瑞阳（1986-），男，福建漳州人，博士，国家数字交换系统工程技术研究中心助理研究员，主要研究方向为文本挖掘、图挖掘。" ]
- 基金信息：
  
  国家自然科学基金创新研究群体基金资助项目;The Foundation for Innovative Research Groups of the National Natural Science Foundation of China(61521003)
- DOI：10.11959/j.issn.2096-109x.2018042
  中图分类号： TP309
- 网络首发：2018-05，
  
  纸质出版：2018-05-15
- 稿件说明：
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曲强, 于洪涛, 黄瑞阳. 基于图卷积网络的社交网络Spammer检测技术[J]. 网络与信息安全学报, 2018,4(5):39-46.

Qiang QU, Hongtao YU, Ruiyang HUANG. Spammer detection technology of social network based on graph convolution network[J]. Chinese Journal of Network and Information Security, 2018, 4(5): 39-46.
曲强, 于洪涛, 黄瑞阳. 基于图卷积网络的社交网络Spammer检测技术[J]. 网络与信息安全学报, 2018,4(5):39-46. DOI： 10.11959/j.issn.2096-109x.2018042.

Qiang QU, Hongtao YU, Ruiyang HUANG. Spammer detection technology of social network based on graph convolution network[J]. Chinese Journal of Network and Information Security, 2018, 4(5): 39-46. DOI： 10.11959/j.issn.2096-109x.2018042.

摘要

在社交网络中，Spammer未经接收者允许，大量地发送对接收者无用的广告信息，严重地威胁正常用户的信息安全与社交网站的信用体系。针对现有社交网络Spammer检测方法的提取浅层特征与计算复杂度高的问题，提出了一种基于图卷积网络（GCN）的社交网络Spammer检测技术。该方法基于网络结构信息，通过引入网络表示学习算法提取网络局部结构特征，结合重正则化技术条件下的 GCN 算法获取网络全局结构特征去检测 Spammer。在 Tagged.com 社交网络数据上进行了实验，结果表明，所提方法具有较高的准确率与效率。

Abstract

In social networks

Spammer send advertisements that are useless to recipients without the recipient's permission

seriously threatening the information security of normal users and the credit system of social networking sites.In order to solve problems of extracting the shallow features and high computational complexity for the existing Spammer detection methods of social networks

a Spammer detection technology based on graph convolutional network(GCN) was proposed.Based on the network structure information

the method introduces the network representation learning algorithm to extract the network local structure feature

and combines the GCN algorithm under the re-regularization technology condition to obtain the network global structure feature to achieve the goal of detecting Spammer.Experiments are done on social network data of Tagged.com.The results show that this method has high accuracy and efficiency.

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Keywords

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