信息网络安全

信息网络安全 ›› 2016, Vol. 16 ›› Issue (9): 158-162.doi: 10.3969/j.issn.1671-1122.2016.09.032

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基于复杂网络的社交媒体内容安全可视化分析系统

周玉晶1,2,3(), 沈嘉荟1, 邱海韬4, 查达仁1   

  1. 1. 中国科学院信息工程研究所,北京 100093
    2. 中国科学院数据与通信保护研究教育中心,北京 100093
    3. 中国科学院大学,北京 100049
    4. 航天长征国际贸易有限公司,北京 100071
  • 收稿日期:2016-07-25 出版日期:2016-09-20 发布日期:2020-05-13
  • 作者简介:

    作者简介： 周玉晶（1991—）,女,河北,博士研究生,主要研究方向为复杂网络、知识图谱;沈嘉荟(1989—),女,辽宁,研究实习员,硕士研究生,主要研究方向为复杂网络、知识图谱;邱海韬（1976—）,男,福建,高级工程师,硕士,主要研究方向为保密通信、组合导航;查达仁（1982—）,男,江苏,高级工程师,博士,主要研究方向为网络安全、密码工程。

  • 基金资助:
    国家高技术研究发展计划（国家863计划）[2013AA01A214];国家重点研发计划[2016YFB0800504]

Complex Network Based Visualization System of Social Media Analysis Content Security

Yujing ZHOU1,2,3(), Jiahui SHEN1, Haitao QIU4, Daren ZHA1   

  1. 1. Institute of Information Engineering, Chinese Academy of Sciences, Beijing 100093, China
    2. Data Assurance and Communication Security Research Center, Chinese Academy of Sciences, Beijing 100093, China
    3. University of Chinese Academy of Sciences, Beijing 100049, China
    4. Aerospace Long-March International Trade Co., Ltd., Beijing 100071, China
  • Received:2016-07-25 Online:2016-09-20 Published:2020-05-13

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摘要：

随着社交网络规模的不断增长,社交媒体逐步成为人们社交生活不可或缺的一部分,使得对社交网络的深度挖掘成为研究社交媒体环境及用户行为的重要手段。因此,对社交媒体舆论内容的安全监管成为了维护社会和谐稳定的重要一环。基于此背景,文章提出了基于复杂网络技术的社交网络抽象表达和分析系统平台,并结合可视化的手段来帮助用户更直观地获取信息。系统包含网络统计特征分析、重点人物发现和社区发现三大主要功能模块,同时以某公司邮件数据为范例进行分析,展示系统的合理性及可用性。

关键词: 复杂网络, 社区发现, 可视化分析

Abstract:

With the drastic explosion of the social network, social media has gradually become the indispensable part of people’s communication life. It rises the importance of social media mining which analyzes on both the social network and individual’s behavior. The supervision of the social media content has become the pivot of maintaining the harmony and stability of the society. According to this background and our observation in this paper, we propose a social media topology expression and analysis system based on the complex network technology, and it utilizes the combination of visualization method in assistance of users obtaining more intuitional information for nasalization and decision making. The system consists of three main analysis modules: network statistical feature analysis, key figure discovery, and community discovery. The three parts’ complementary combination also clearly shows connections and logical relation with each other. Finally, we demonstrate the rationality and availability of the system by the case analysis of a company.

Key words: complicated network, community discovery, visualization analysis

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