基于区域交互模型的SNS网络用户影响力评估

王楠; 孙钦东; 周亚东; 王汉秦; 隋连升

doi:10.11959/j.issn.1000-436x.2016020

您当前的位置：

首页 >

文章列表页 >

基于区域交互模型的SNS网络用户影响力评估

学术论文 | 更新时间：2024-06-05

- 基于区域交互模型的SNS网络用户影响力评估
- Study on user influence analysis via regional user interaction model in online social networks
- 通信学报 2016年37卷第1期页码：160-169
- 作者机构：
  
  1. 西安理工大学网络计算与安全技术陕西省重点实验室，陕西西安 710048
  2. 西安交通大学智能网络与网络安全教育部重点实验室，陕西西安 710049
- 作者简介：
  
  [ "王楠（1983-），男，河南安阳人，西安理工大学博士生，主要研究方向为在线社会网络、数据挖掘等。" ]
  [ "孙钦东（1975-），男，山东莒南人，博士，西安理工大学教授，主要研究方向为网络安全、在线社会网络、物联网等。" ]
  [ "周亚东（1982-），男，陕西汉中人，博士，西安交通大学讲师，主要研究方向为在线社会网络、Web挖掘等。" ]
  [ "王汉秦（1987-），男，陕西西安人，西安理工大学硕士生，主要研究方向为在线社会网络。" ]
  [ "隋连升（1972-），男，陕西韩城人，博士，西安理工大学副教授，主要研究方向为计算机图形学、数字图像处理以及计算机视觉等。" ]
- 基金信息：
  
  国家自然科学基金资助项目(61172124);国家自然科学基金资助项目(61571360);国家自然科学基金资助项目(61202392)
- DOI：10.11959/j.issn.1000-436x.2016020
  中图分类号： TP393
- 网络出版日期：2016-01，
  
  纸质出版日期：2016-01-25
- 稿件说明：
移动端阅览
王楠, 孙钦东, 周亚东, 等. 基于区域交互模型的SNS网络用户影响力评估[J]. 通信学报, 2016,37(1):160-169.

Nan WANG, dong SUNQin, dong ZHOUYa, et al. Study on user influence analysis via regional user interaction model in online social networks[J]. Journal on communications, 2016, 37(1): 160-169.
王楠, 孙钦东, 周亚东, 等. 基于区域交互模型的SNS网络用户影响力评估[J]. 通信学报, 2016,37(1):160-169. DOI： 10.11959/j.issn.1000-436x.2016020.

Nan WANG, dong SUNQin, dong ZHOUYa, et al. Study on user influence analysis via regional user interaction model in online social networks[J]. Journal on communications, 2016, 37(1): 160-169. DOI： 10.11959/j.issn.1000-436x.2016020.

摘要

针对现有方法与模型未能准确体现不同距离用户之间真实交互行为的问题，提出了一种基于用户区域交互模型的用户影响力评估方法。区域交互模型利用影响力传递的不同方式，刻画不同距离之间用户的交互行为模式，能更为真实准确地反映在线社会网络用户之间的交互行为。通过计算用户对相邻用户的显性影响力与非相邻用户的隐性影响力，可有效识别在线社会网络中大影响力用户、僵尸粉用户等不同类型用户。基于新浪微博与人人网真实数据开展用户影响力评估以及相应的用户角色识别实验，结果显示，与现有方法相比，基于区域交互模型的识别方法可以准确有效地识别出在线社会网络中的大影响力用户、僵尸粉用户等各类型用户

Abstract

Conventional user influence researches do not accurate reflect the real interaction pattern between different users in online social networks. In order to solve this problem

a user influence evaluation method based on regional user interaction model has been proposed. The regional user interaction model can illustrate the real online social network user interaction pattern between users with different distance by the influence transfer effect. The method calculates the direct influence and the indirect influence of each user in online social networks and identifies the influential users and zombie users. Experiments are based on the real data of Sina Weibo and RenRen online social networks and the results show that compared with the existing methods the method has better accuracy and efficiency for the infl tial user and zombie us-er identification.

关键词

Keywords

references

KANNA A F , YACINE A , AJITH A . Models of influence in online social networks [J ] . International Journal of Intelligent Systems , 2013 , 29 ( 2 ): 161 - 183 .

LIM S H , KIM S W , PARK SUN J . Determining content power users in a blog network: an approach and its applications [J ] . IEEE Transac-tions on Systems, Man, and Cybernetics, Part A: Systems and Humans Archive , 2011 , 41 ( 5 ): 853 - 862 .

LI X , CHENG S Y , CHEN W L . Novel user influence measurement, based on user interaction in microblog [C ] // The 2013 IEEE/ACM In-ternational Conference on Advances in Social Networks Analysis and MiningNiagara Falls . Canada . c 2013 : 615 - 619 .

WU X M , WANG J M . Micro-blog in China: identify influential users and automatically classify posts on Sina micro-blog [J ] . Journal of Ambient Intelligence and Humanized Computing , 2014 , 5 ( 1 ): 51 - 63 .

CHA M Y , HADDADI H , BENEVENUTO F . Measuring user influ-ence in Twitter: the million follower fallacy [C ] // The Fourth Interna-tional AAAI Conference on Weblogs and Social Media . Washington, DC, USA . c 2010 : 10 - 18 .

LEAVITT A , BURCHARD E , FISHER D , et al . The Influentials:New Approaches for Analyzing Influence on Twitter [R ] . Web Ecolo-gy Project , 2009

KITSAK M , GALLOS L K , HAVLIN S . Identification of influential spreaders in complex networks [J ] . Nature Physics , 2010 , 6 ( 11 ): 888 - 893 .

BROWN P , FENG J L . Measuring user influence on Twitter using modified K -shell decomposition [C ] // The 2011 ICWSM Workshop on the Social Mobile Web . Barcelona, Spain . c 2011 : 18 - 23 .

CHEN D B , LV L Y , SHANG M S . Identifying influential nodes in complex networks [J ] . Physica A: Statistical Mechanics and its Appli-cations , 2012 , 391 ( 4 ): 1777 - 1787 .

HUANG Y L , LI L . Analysis of user influence in social network based on behavior and relationship [C ] // The 2nd International Conference on Measurement, Information and Control . Harbin, China . c 2013 : 682 - 686 .

TANG X N , YANG C C . Ranking user influence in healthcare social media [J ] . ACM Transactions on Intelligent Systems and Technology , 2012 , 3 ( 4 ): 565 - 582 .

UYSAL I , CRFOFT W B . User oriented tweet ranking: a filtering approach to microblogs [C ] // The 20th ACM International Conference on Information and knowledge Management . Glasgow, Scotland . c 2011 : 2261 - 2264 .

SUN B M , VINCENT T Y . Identifying influential users by their post-ings in social networks [C ] // The23rd ACM Conference on Hypertext and Social Media Workshop on Modeling Social Media . Milwaukee, USA , c 2012 : 1 - 8 .

STRINGHINI G , KRUEGEL C , VIGNA G . Detecting spammers on social networks [C ] // The 26th Annual Computer Security Applications Conference . New York, NY, USA: ACM , c 2010 : 1 - 9 .

CHU Z , GIANVECCHIO S , WANG H N . Detecting automation of Twitter accounts: are you a human, bot, or cyborg [J ] . IEEE Transactions on Dependable and Secure Computing , 2012 , 9 ( 6 ): 811 - 824 .

BHAT S Y , ISLAMIA J M , DELHI N . Community-based features for identifying spammers in online social networks [C ] // The 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining . Niagara Falls,Canada , c 2013 : 100 - 107 .

SUN Q D , WANG N , ZHOU Y D , et al . Modeling for user Interaction by influence transfer effect in online social networks [C ] // The 39th IEEE Conference on Local Computer Networks . Edmonton, Canada , c 2014 : 486 - 489 .

LIANG H , LU G , XU N S . Analyzing user influence of micro-blog [C ] // 2012 IEEE fifth International Conference on Advanced Computational Intelligence (ICACI). Nanjing, China , c 2012 : 15 - 22 .

BAKSHY E , HOFMAN J M , MASON W A , et al . Everyone's an influencer: quantifying influence on Twitter [C ] // The 4th ACM Inter-national Conference on Web Search ＆ Data Mining . HongKong, China , c 2011 : 65 - 74 .

浏览量

712

下载量

CSCD

文章被引用时，请邮件提醒。

提交

工具集

关联资源

基于交互行为的在线社会网络水军检测方法