通信学报 ›› 2019, Vol. 40 ›› Issue (10): 1-9.doi: 10.11959/j.issn.1000-436x.2019207

• 学术论文 •    下一篇

TSL:基于连接强度的Facebook消息流行度预测模型

王晓萌1, 方滨兴1,2, 张宏莉1, 王星1   

  1. 1 哈尔滨工业大学计算机网络与信息安全技术研究中心,黑龙江 哈尔滨 150001
    2 广州大学网络空间先进技术研究院,广东 广州 510006
  • 修回日期:2019-08-30 出版日期:2019-10-25 发布日期:2019-11-07
  • 作者简介:王晓萌(1987- ),男,黑龙江哈尔滨人,哈尔滨工业大学博士生,主要研究方向为在线社交网络、信息传播预测、舆情安全等。|方滨兴(1960- ),男,江西万年人,中国工程院院士,哈尔滨工业大学教授、博士生导师,主要研究方向为计算机网络与信息安全理论与技术、并行计算等。|张宏莉(1973- ),女,吉林榆树人,博士,哈尔滨工业大学教授、博士生导师,主要研究方向为网络与信息安全、网络测量与建模、网络计算、并行处理等。|王星(1981- ),男,重庆人,博士,哈尔滨工业大学助理研究员,主要研究方向为网络与信息安全、网络舆情监控、知识迁移。
  • 基金资助:
    国家重点研发计划基金资助项目(2017YFB0803305);国家重点研发计划基金资助项目(2016QY03D0501)

TSL:predicting popularity of Facebook content based on tie strength

Xiaomeng WANG1, Binxing FANG1,2, Hongli ZHANG1, Xing WANG1   

  1. 1 Research Center of Computer Network and Information Security Technology,Harbin Institute of Technology,Harbin 150001,China
    2 Cyberspace Institute of Advanced Technology,Guangzhou University,Guangzhou 510006,China
  • Revised:2019-08-30 Online:2019-10-25 Published:2019-11-07
  • Supported by:
    The National Key R&D Program of China(2017YFB0803305);The National Key R&D Program of China(2016QY03D0501)

摘要:

在线社交网络的迅速发展使信息呈现爆炸式增长,然而不同消息的流行度存在较大差异,对其准确预测一直是领域内的研究难点。流行度预测的任务是根据消息传播早期过程中涌现的特征预测其未来的传播趋势,现有基于传播网络特征与拟合函数的预测模型难以解决预测准确率低的问题,因此借助社会学中的弱连接理论,引入连接强度的概念,并融合消息传播早期的流行度构建多元线性回归方程,提出了一种针对 Facebook 知名主页的消息流行度的预测模型TSL。通过在Facebook真实数据集(含154万次转发)上与其他具有代表性的基准模型进行比较,实验表明TSL模型可以对消息的最终转发流行度进行有效预测,预测性能优于同类方法。

关键词: 在线社交网络, 弱连接, 流行度, 信息传播

Abstract:

The rapid development of online social networks leads to an explosion of information,however,there are great differences in the popularity of different messages,and accurate prediction is always a great difficulty is the current study.Popularity prediction of online content aims to predict the popularity in the future based on its early diffusion status.Existing models for popularity prediction were mostly based on discovering network features or fitting the equation into a varying time function that the accuracy of current popularity prediction model was not high enough.Therefore,with the help of the weak ties theory in sociology,the concept of tie strength was introduced and a multilinear regression equation was constructed combined with the early popularity.A TSL model to predict the popularity of Facebook’s well-known pages was proposed.The main contribution of this article was to solve the problem and few or no work based on sociology.A high linear correlation between the proportion of faithful fans was existed in Facebook homepage with frequent shares in the early and the future popularity.Compared with other baseline models,an experimental study of Facebook (including 1.54 million shares) illustrates the effectiveness of the proposed TSL model,and the performance is better than the existing similar methods.

Key words: online social networks, weak ties, popularity, information diffusion

中图分类号: 

[1] . 联想服务器助力好耶广告网络邮件系统[J]. 电信科学, 2009, 25(11): 107 .
[2] 黄俊杰,孙力娟,王汝传,黄海平. 基于虚拟势场和覆盖影响因子的三维传感器网络覆盖增强算法[J]. 通信学报, 2010, 31(9A): 3 -21 .
[3] 杨付正,万 帅. 网络视频质量评估技术研究现状及发展动向[J]. 通信学报, 2012, 33(4): 15 -114 .
[4] 孙文邦,唐海燕,孙文斌,程 红. 基于变换基阵的三维SDCT表示方法[J]. 通信学报, 2012, 33(4): 22 -168 .
[5] 吕少卿1,张玉清1,2,倪平2. 基于公开信息的社交网络隐私泄露[J]. 通信学报, 2013, 34(Z1): 25 -196 .
[6] 唐朝霞,徐秋亮,朱建栋. 基于身份的指定多个验证者的代理签名方案[J]. 通信学报, 2008, 29(11A): 8 -45 .
[7] 陈立南,刘 阳,马 严,黄小红,赵庆聪,魏 伟. 基于统计的高效决策树分组分类算法[J]. 通信学报, 2014, 35(Z1): 12 -64 .
[8] 程 龙,吴成东,张云洲,贾子熙,纪 鹏. 基于二元传感器网络的多源定位研究[J]. 通信学报, 2011, 32(10): 20 -165 .
[9] 赵广松,陈 鸣. 自私性机会网络中激励感知的内容分发的研究[J]. 通信学报, 2013, 34(2): 9 -84 .
[10] 李元杰,杨绿溪,何振亚. 基于训练序列的MIMO信道估计[J]. 通信学报, 2006, 27(5): 1 -5 .