社区环境下基于节点交互和主题的影响力计算模型

doi:10.3969/j.issn.0372-2112.2020.03.023

电子学报 ›› 2020, Vol. 48 ›› Issue (3): 582-589.DOI: 10.3969/j.issn.0372-2112.2020.03.023

社区环境下基于节点交互和主题的影响力计算模型

王大刚¹, 钟锦^1,2, 吴昊¹

1. 合肥师范学院计算机学院, 安徽合肥 230601;
2. 中国科学技术大学计算机学院, 安徽合肥 230600

收稿日期:2019-03-06 修回日期:2019-07-03 出版日期:2020-03-25
- 作者简介:
- 王大刚 男,1982年10月出生,安徽肥东人,讲师,2008年毕业于安徽大学获硕士学位,主要研究方向:数据挖掘,智能信息处理,Web与数据库E-mail:wdgx621@163.com;钟锦男,1973年6月出生,安徽舒城人,教授,分别于2004和2008在合肥工业大学和中国科学技术大学获硕士和博士学位,主要研究方向:人工智能,博弈论,智能信息处理;吴昊男,1983年5月出生,安徽舒城人,副教授,研究方向:智能视频处理与分析
- 基金资助:
- 安徽省自然科学基金 (No.1708085QF157）; 安徽省高校优秀青年人才支持计划 (No.gxyq2017050）; 安徽省教育教学委托研究项目 (No.2018jyxm1470）; 国家大学生创新创业项目 (No.201914098034）

Influence Calculation Model Based on Node Interaction and Topic in Community Environment

WANG Da-gang¹, ZHONG Jin^1,2, WU Hao¹

1. School of Computer Science and Technology, Hefei Normal University, Hefei, Anhui 230601, China;
2. School of Computer Science and Technology, University of Science and Technology of China Hefei, Anhui 230600, China

Received:2019-03-06 Revised:2019-07-03 Online:2020-03-25 Published:2020-03-25
- Supported by:
- Natural Science Foundation of Anhui Province (No.1708085QF157); Excellent Young Talents Project in Higher Education of Anhui Province (No.gxyq2017050); Research Project Commissioned by Department of Education of Anhui Province (No.2018jyxm1470); National College Students Innovation and Entrepreneurship Training Program (No.201914098034)

摘要/Abstract

摘要： 为解决现有算法对社交网络节点影响力计算准确度不高的问题，本文整合节点不同维度信息，综合考虑节点在多个主题社区上的主题分布向量，提出一种新的节点影响力计算模型.模型首先将主题相关性作为先验信息；然后利用混合隶属度随机块（Mixed Membership Stochastic Block）模型表达节点间的交互关系，用主题模型学习主题内容；最后结合全局拓扑关系迭代计算节点的全局影响力.本文选取社交网络数据，以P@N、MAP等作为评价指标同现有主流算法进行比较.实验结果显示，本文算法有效提升了影响力节点识别的准确度和排名的有效性.

关键词: 主题, 影响力, 混合隶属度随机块, 先验

Abstract: To solve the accuracy problems of the existing algorithms in calculating the influence of social network nodes, by integrating different dimension information of nodes, and considering the topic distribution vector of nodes on multiple communities, a new model is proposed. It first regards the correlation between topics as the prior information, then uses the mixed membership stochastic block (MMSB) model to express the interaction among nodes, learns topic contents using topic model, and finally, iteratively calculates the global influence of nodes with global topological relationship. We select data from social networks, use P@N, MAP, etc., as the evaluation indicators, and compare the proposed algorithm with the existing mainstream algorithms. The experimental results show that our algorithm significantly improves the identification accuracy of influential nodes and the validity of ranking.

Key words: topic, influence, mixed membership stochastic block, prior

中图分类号:

TP311

王大刚, 钟锦, 吴昊. 社区环境下基于节点交互和主题的影响力计算模型[J]. 电子学报, 2020, 48(3): 582-589.

WANG Da-gang, ZHONG Jin, WU Hao . Influence Calculation Model Based on Node Interaction and Topic in Community Environment[J]. Acta Electronica Sinica, 2020, 48(3): 582-589.

参考文献

[1] Kleinberg JM.Authoritative sources in a hyperlinked env-ironment[J].Journal of the ACM,1999,46(5):604-632.
[2] Li Q,Zhou T,Lü L,et al.Identifying influential spreaders by weighted LeaderRank[J].Physica A:Statistical Mechanics and Its Applications,2014,404:47-5.
[3] Barbieri N,Bonchi F,Manco G.Topic-aware social inf-luence propagation models[J].Knowledge and information systems,2013,37(3):555-584.
[4] Chen W,Lin T,Yang C.Efficient topic-aware influence maximization using preprocessing[J].The Computing Res-earch Repository,2014,9197:1-13.
[5] H Zhao,X Xu,Y Song,et al.Ranking users in social networks with higher-order structures[A].The Thirty-Second AAAI Conference on Artificial Intelligence[C].Stroudsburg:ACL,2018.1073-1083.
[6] Weng J,Lim EP,Jiang J,et al.Twitterrank:Finding topic-sensitive influential twitterers[A].Proceedings of the 3rd ACM International Conference on Web Search and Data Mining[C].NewYork:ACM,2010.261-270.
[7] Dietz L,Bickel S,Scheffer T.Unsupervised prediction of citation influences[A].Proceedings of the 24th International Conference on Machine Learning[C].Stroudsburg:ACL,2007.233-240.
[8] 张腊梅,黄威靖,陈薇,等.EMTM:微博中与主题相关的专家挖掘方法[J].计算机研究与发展,2015,52(11):2517-2526. ZHANG La-mei,HUANG Wei-qian,CHEN Wei,et al.EMTM:An expert mining method related to topics inmicroblog[J].Computer Research and Development,2015,52(11):2517-2526.(in Chinese)
[9] 张仰森,郑佳,唐安杰.基于多特征融合的微博用户权威度定量评价方法[J].电子学报,2017,45(11),2800-2809 ZHANG Yang-sen,ZHENG Jia,TANG An-jie.Quantitative evaluation method of microblog user authority based on multi-feature fusion[J].Acta Electronica Sinica,2017,45(11),2800-2809.(in Chinese)
[10] Ding ZY,Zhou B,Jia Y,et al.Topical influence analysis based on the multi-relational network in micr-oblogs[J].Journal of Computer Research and Develop-ment,2013,50(10):2155-217.
[11] 曹玖新,陈高君,吴江林.基于多维特征分析的社交网络意见领袖挖掘[J].电子学报,2016,44(4):898-90. CAOJiu-xin,CHENG Gao-jun,Wu Jiang-lin.Social net-work opinion leader mining based on multidimensional feature analysis[J].Acta Electronica Sinica,2016,44(4):898-90.(in Chinese)
[12] Jonathan Chang,David M Blei.Hierarchical relational models for document networks[J].The Annals of Applied Statistics,2010,4(1):124-150.
[13] David M Blei,John D Lafferty.Correlated topic models[A].Proceedings of the 18th International Conference on Neural Information Processing System[C].Canada:MIT Press,200.147-154.
[14] Tang J,Sun J,Wang C,et al.Social influence analysis in large-scale networks[A].Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining[C].Paris:ACM,2009.807-816.

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社区环境下基于节点交互和主题的影响力计算模型

Influence Calculation Model Based on Node Interaction and Topic in Community Environment

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