话题感知下的跨社交网络影响力最大化分析
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摘要
随着各种社交网站的不断涌现,在多社交网络上找到影响传播范围最大的一组用户,对产品推荐或产品推广具有重要作用。为提高产品推荐或推广的广度和精准性,提出了一种跨社交网络基于话题感知的影响力最大化处理方法M-TLTGreedy。首先,根据跨社交网络中的文本语义信息和用户间的社会关系来评价多社交网络中用户间关系,以此构建一个基于话题的跨社交网络图;然后,在线性阈值模型的基础上,设计了一个基于话题感知的跨社交网络影响力最大化模型M-TLT(multiple-topic linear threshold);接着,基于M-TLT模型,利用改进的启发式算法,进行初始用户集的选取;最后,基于大量数据集的实验,证明了该算法无论在影响范围和时间效率上均表现良好。
With the continuous emergence of various social networking sites, finding a group of the most influential users on multiple social networks is very important for product recommendation or product promotion. In order to improve the breadth and accuracy of product recommendation or promotion, this paper presents an algorithm of topicaware influence maximization, M-TLTGreedy. Firstly, this paper evaluates the relation among users based on their text semantics and social relationships in multiple social networks to build a topic-based cross-network graph. Then,based on the linear threshold model, this paper designs a topic-aware influence maximization model across social networks, M-TLT(multiple-topic linear threshold) model. Next, this paper uses the improved heuristic algorithm to select a set of users based on the M-TLT model. Finally, the extensive experiments on real datasets show that the M-TLTGreedy algorithm performs well on influence spread and running time.
With the continuous emergence of various social networking sites, finding a group of the most influential users on multiple social networks is very important for product recommendation or product promotion. In order to improve the breadth and accuracy of product recommendation or promotion, this paper presents an algorithm of topicaware influence maximization, M-TLTGreedy. Firstly, this paper evaluates the relation among users based on their text semantics and social relationships in multiple social networks to build a topic-based cross-network graph. Then,based on the linear threshold model, this paper designs a topic-aware influence maximization model across social networks, M-TLT(multiple-topic linear threshold) model. Next, this paper uses the improved heuristic algorithm to select a set of users based on the M-TLT model. Finally, the extensive experiments on real datasets show that the M-TLTGreedy algorithm performs well on influence spread and running time.
引文
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[13]Li Guoliang,Chu Yaping,Feng Jianhua,et al.Influence maximization on multiple social networks[J].Chinese Journal of Computers,2016,39(4):643-656.
[14]Brown J J,Reinegen P H.Social ties and word-of-mouth referral behavior[J].Journal of Consumer Research,1987,14(3):350-362.
[15]Richardson M,Domingos P M.Mining knowledge-sharing sites for viral marketing[C]//Proceedings of the 8th ACMSIGKDD International Conference on Knowledge Discovery and Data Mining,Edmonton,Jul 23-26,2002.New York:ACM,2002:61-70.
[16]Cao Xuezhi,Yu Yong.ASNets:a benchmark dataset of aligned social networks for cross-platform user modeling[C]//Proceedings of the 25th ACM International Conference on Information and Knowledge Management,Indianapolis,Oct24-28,2016.New York:ACM,2016:1881-1884.
[17]Guo Weiyu,Wu Shu,Wang Liang,et al.Social-relational topic model for social networks[C]//Proceedings of the 24th ACM International Conference on Information and Knowledge Management,Melbourne,Oct 19-23,2015.New York:ACM,2015:1731-1734.
[13]李国良,楚娅萍,冯建华,等.多社交网络的影响力最大化分析[J].计算机学报,2016,39(4):643-656.
[2]Leskovec J,Krause A,Guestrin C,et al.Cost-effective outbreak detection in networks[C]//Proceedings of the 13th ACMSIGKDD International Conference on Knowledge Discovery and Data Mining,San Jose,Aug 12-15,2007.New York:ACM,2007:420-429.
[3]Goyal A,Lu Wei,Lakshmanan L V S.CELF++:optimizing the greedy algorithm for influence maximization in social networks[C]//Proceedings of the 20th International Conference on World Wide Web,Hyderabad,Mar 28-Apr 1,2011.New York:ACM,2011:47-48.
[4]Chen Wei,Wang Chi,Wang Yajun.Scalable influence maximization for prevalent viral marketing in large-scale social networks[C]//Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining,Washington,Jul 25-28,2010.New York:ACM,2010:1029-1038.
[5]Goyal A,Lu Wei,Lakshmanan L V S.SIMPATH:an efficient algorithm for influence maximization under the linear threshold model[C]//Proceedings of the 11th IEEE International Conference on Data Mining,Vancouver,Dec 11-14,2011.Washington:IEEE Computer Society,2011:211-220.
[6]Tang Jie,Sun Jimeng,Wang Chi,et al.Social influence analysis in large-scale networks[C]//Proceedings of the 15th ACMSIGKDD International Conference on Knowledge Discovery and Data Mining,Paris,Jun 28-Jul 1,2009.New York:ACM,2009:807-816.
[7]Aslay?,Barbieri N,Bonchi F,et al.Online topic-aware influence maximization queries[C]//Proceedings of the 17th International Conference on Extending Database Technology,Athens,Mar 24-28,2014:295-306.
[8]Barbieri N,Bonchi F,Manco G.Topic-aware social influence propagation models[C]//Proceedings of the 12th International Conference on Data Mining,Brussels,Dec 10-13,2012.Washington:IEEE Computer Society,2012:81-89.
[9]Chen Shou,Fan Ju,Li Guoliang,et al.Online topic-aware influence maximization[C]//Proceedings of the 41st International Conference on Very Large Data Bases,Kohala Coast,Aug 31-Sep 4,2015:666-677.
[10]Chu Yaping,Zhao Xianghui,Liu Sitong,et al.An efficient method for topic-aware influence maximization[C]//LNCS8709:Proceedings of the 16th Asia-Pacific Web Conference on Web Technologies and Applications,Changsha,Sep 5-7,2014.Berlin,Heidelberg:Springer,2014:584-592.
[11]Nguyen D T,Zhang Huiyuan,Das S,et al.Least cost influence in multiplex social networks:model representation and analysis[C]//Proceedings of the 13th International Conference on Data Mining,Dallas,Dec 7-10,2013.Washington:IEEE Computer Society,2013:567-576.
[12]Zhang Huiyuan,Nguyen D T,Zhang Huiling,et al.Least cost influence maximization across multiple social networks[J].IEEE/ACM Transactions on Networking,2016,24(2):929-939.
[13]Li Guoliang,Chu Yaping,Feng Jianhua,et al.Influence maximization on multiple social networks[J].Chinese Journal of Computers,2016,39(4):643-656.
[14]Brown J J,Reinegen P H.Social ties and word-of-mouth referral behavior[J].Journal of Consumer Research,1987,14(3):350-362.
[15]Richardson M,Domingos P M.Mining knowledge-sharing sites for viral marketing[C]//Proceedings of the 8th ACMSIGKDD International Conference on Knowledge Discovery and Data Mining,Edmonton,Jul 23-26,2002.New York:ACM,2002:61-70.
[16]Cao Xuezhi,Yu Yong.ASNets:a benchmark dataset of aligned social networks for cross-platform user modeling[C]//Proceedings of the 25th ACM International Conference on Information and Knowledge Management,Indianapolis,Oct24-28,2016.New York:ACM,2016:1881-1884.
[17]Guo Weiyu,Wu Shu,Wang Liang,et al.Social-relational topic model for social networks[C]//Proceedings of the 24th ACM International Conference on Information and Knowledge Management,Melbourne,Oct 19-23,2015.New York:ACM,2015:1731-1734.
[13]李国良,楚娅萍,冯建华,等.多社交网络的影响力最大化分析[J].计算机学报,2016,39(4):643-656.