基于频繁传播模式的影响群落发现方法

吉林大学学报(工学版) ›› 2013, Vol. 43 ›› Issue (02): 444-450.

基于频繁传播模式的影响群落发现方法

刘丽娜¹, 沈继红^1,2, 朱强华³, 丁兆云⁴

1. 哈尔滨工程大学自动化学院, 哈尔滨 150001;
2. 哈尔滨工程大学理学院, 哈尔滨 150001;
3. 海军工程大学电子工程学院, 武汉 430033;
4. 国防科技大学计算机学院, 长沙 410073

收稿日期:2012-06-05 出版日期:2013-03-01 发布日期:2013-03-01
作者简介:刘丽娜(1985-),女,博士研究生.研究方向:复杂系统建模,系统可靠性与优化,数据分析.E-mail:liulinazb@163.com
基金资助:
国家自然科学基金项目(61202127).

Discovering tribe-leaders based on frequent pattern of propagation

LIU Li-na¹, SHEN Ji-hong^1,2, ZHU Qiang-hua³, DING Zhao-yun⁴

1. College of Automation, Harbin Engineering University, Harbin 150001, China;
2. College of Science, Harbin Engineering University, Harbin 150001, China;
3. College of Electronic Engineering, Naval University of Engineering, Wuhan 430033, China;
4. College of Computer, National University of Defense Technology, Changsha 410073, China

Received:2012-06-05 Online:2013-03-01 Published:2013-03-01

摘要/Abstract

摘要： 针对传统研究大多基于影响个体挖掘而忽略了影响群落的发现,本文考虑用户之间的频繁传播模式,提出了一种基于频繁传播模式的影响群落挖掘方法。针对群落内部传播模式的多样化,给出了一种信息传播树扩展方法,通过松弛信息传播树有向特性与图扩展方法,将信息传播树转换为连通无向无环图。结合支持度与影响强度,提出了一种新的频繁子图挖掘算法Tribe-FGM,减小模式增长的规模,提高频繁子图挖掘效率。实验采用新浪微博真实数据,在约90万条博文以及对应约64万左右用户的"地震"话题与约31万条博文以及对应约21万左右用户的"两会"话题的数据集上验证了算法的性能和有效性。

关键词: 计算机应用, 社会网络, 频繁模式, 影响力

Abstract: A novel scheme of mining tribe-leaders was proposed based on the frequent pattern of propagation. In this scheme, first, a method to expend the information tree is applied to overcome the problem of multi-pattern propagation, in which the information propagation tree is converted into a connected and undirected acyclic graph. Then, considering the support and influent strength, a new frequent sub-graph mining method called Tribe-FGM is proposed to improve the efficiency of the graph mining by reducing the scale of pattern growth. A real dataset from sina microblog was taken in the experiment. The dataset is about topic of "earthquake", which contains 0.9 million posts and 0.6 million users, and the topic of the "two sessions", which contains about 0.31 million posts and 0.21 users. Experiment results validate the proposed scheme.

Key words: computer application, social network, frequent pattern, influence

中图分类号:

TP391

刘丽娜, 沈继红, 朱强华, 丁兆云. 基于频繁传播模式的影响群落发现方法[J]. 吉林大学学报(工学版), 2013, 43(02): 444-450.

LIU Li-na, SHEN Ji-hong, ZHU Qiang-hua, DING Zhao-yun. Discovering tribe-leaders based on frequent pattern of propagation[J]. 吉林大学学报(工学版), 2013, 43(02): 444-450.

参考文献

[1] Weimann G. The Influentials: People Who Influence People[M]. Albany: State University of New York Press, 1994.

[2] Kempe D, Kleinberg J, Tardos . Maximizing the spread of influence through a social network//Proc of the 9th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York: ACM, 2003.

[3] Chen Wei, Wang Chi, Wang Ya-jun. Scalable influence maximization for prevalent viral marketing in large scale social networks//Proc of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York: ACM, 2010.

[4] Naohiro M, Yukio O, Mitsuru I. Influence diffusion model in text-Based communication[J]. Transactions of the Japanese Society for Artificial Intelligence, 2002, 17(3): 259-267.

[5] Song Xiaodan, Chi Yun, Koji Hino, et al. Identifying opinion leaders in the blogosphere//Proc of the 16th ACM conference on Conference on Information and Knowledge Management. New York: ACM, 2007.

[6] Pal A, Counts S. Identifying topical authorities in microblogs//Proc of the 4th ACM International Conference on Web Search and Data Mining. New York: ACM, 2011.

[7] Yan X, Han J. gSpan: Graph-based substructure patterns mining//Proce of the 2nd International Conference on Data Mining. NJ: IEEE,2002.

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