基于能量优化的微博用户转发行为预测

doi:10.3969/j.issn.0372-2112.2017.12.022

电子学报 ›› 2017, Vol. 45 ›› Issue (12): 2987-2996.DOI: 10.3969/j.issn.0372-2112.2017.12.022

基于能量优化的微博用户转发行为预测

王伟, 张效尉, 任国恒, 秦东霞, 刘琳琳

周口师范学院网络工程学院, 河南周口 466000

收稿日期:2016-06-30 修回日期:2016-11-16 出版日期:2017-12-25
- 作者简介:
- 王伟,男,1976年10月生,河南周口人.周口师范学院副教授,主要研究方向为计算机视觉、社交网络分析等.E-mail:wangwei@zknu.cn;张效尉,男,1982年12月生,河南开封人.2009年毕业于郑州轻工业学院,获硕士学位.目前为周口师范学院讲师,主要研究方向为数据挖掘、社交网络分析;任国恒,男,1982年4月生,河南周口人,2011年6月毕业于西安工业大学,硕士学位.目前为周口师范学院讲师,研究方向为社交网络分析;秦东霞,女,1983年7月生,河南周口人.2009年毕业于重庆大学,获硕士学位.目前为周口师范学院讲师,主要研究方向为数据挖掘、社交网络分析;刘琳琳,女,1986年11月生,河南洛阳人.2013年毕业上海大学,获硕士学位.目前为周口师范学院助教.主要研究方向为数据挖掘、社交网络分析.
- 基金资助:
- 国家自然科学基金 (No.U1404620，No.U1404622）; 河南省自然科学基金 (No.162300410347）; 河南省科技攻关项目 (No.172102310727，No.162102310589，No.162102210396，No.162102310590）; 河南省高校重点科研项目 (No.17A520018，No.17A520019，No.15A520116，No.16B520034，No.16A520105）; 周口师范学院高层次人才科研启动基金 (No.zknuc2015103）

Predicting Microblog User Retweet Behaviors Based on Energy Optimization

WANG Wei, ZHANG Xiao-wei, REN Guo-heng, QIN Dong-xia, LIU Lin-lin

School of Network Engineering, Zhoukou Normal University, Zhoukou, Henan 466000, China

Received:2016-06-30 Revised:2016-11-16 Online:2017-12-25 Published:2017-12-25
- Supported by:
- National Natural Science Foundation of China (No.U1404620, No.U1404622); Natural Science Foundation of Henan Province, China (No.162300410347); Science and Technology Research and Development Program of Henan Province (No.172102310727, No.162102310589, No.162102210396, No.162102310590); Key Research Program of Universities and Colleges in Henan Province (No.17A520018, No.17A520019, No.15A520116, No.16B520034, No.16A520105); Zhoukou Normal University High-level Talents Research Foundation (No.zknuc2015103)

摘要/Abstract

摘要： 微博用户转发行为预测是微博社交网络消息扩散模型构建的基础，在舆情监控、市场营销与政治选举等领域有着广泛的应用.为了提高用户转发行为预测的精度，本文在MRF（Markov Random Field）能量优化框架下综合分析了用户属性与微博内容特征、用户转发行为约束、群体转发先验等因素对用户转发行为的影响，并在逻辑回归模型的基础上构造了相应的能量函数对用户转发行为进行了全局性的预测.实验结果表明，微博用户转发行为不仅取决于用户属性、微博内容等特征，而且也受到用户转发行为约束、群体转发先验等因素不同程度的影响.相对于传统算法，本文算法可以更准确地对用户转发行为进行建模，因而可获得更好的预测结果.

关键词: 新浪微博, 转发预测, 能量优化, 逻辑回归

Abstract: Predicting user retweet behaviors is the basis of building the information diffusion model in micorblog social networks,and also is applied for a variety of fields such as public opinion monitoring,viral marketing,political campaign etc.In order to improve the accuracy of predicting user retweet behaviors,under the MRF (Markov Random Field) framework,the paper comprehensively analyzes the effects caused by user attributes,microblog contents,the constraints between user retweet behaviors and the group retweet priors,and constructs the corresponding energy function based on the logistic regression model to globally predict user retweet behaviors.Experimental results show user retweet behaviors not only depend on user attributes,and micorblog contents,but also are influenced by the constraints between user retweet behaviors and the group retweet priors in varying degrees.Compared to the traditional methods,our proposed method can accurately model user retweet behaviors and thus achieve satisfactory results.

Key words: sina microblog, retweet predicting, energy optimization, logistic regression

中图分类号:

TP391

王伟, 张效尉, 任国恒, 等. 基于能量优化的微博用户转发行为预测[J]. 电子学报, 2017, 45(12): 2987-2996.

WANG Wei, ZHANG Xiao-wei, REN Guo-heng, et al. Predicting Microblog User Retweet Behaviors Based on Energy Optimization[J]. Acta Electronica Sinica, 2017, 45(12): 2987-2996.

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基于能量优化的微博用户转发行为预测

Predicting Microblog User Retweet Behaviors Based on Energy Optimization

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