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基于最小点覆盖和反馈点集的社交网络影响最大化算法 被引量：7

Minimum Vertex Covering and Feedback Vertex Set-based Algorithm for Influence Maximization in Social Network
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摘要 社交网络中的影响最大化问题是指在特定的传播模型下,如何寻找k个最具影响力的节点使得在该模型下社交网络中被影响的节点最多,信息传播的范围最广。该问题是一个优化问题,并且已经被证明是NP-难的。考虑到图的最小点覆盖和反馈点集中的顶点对图的连通性影响较大,该文提出一种基于最小点覆盖和反馈点集的社交网络影响最大化算法(Minimum Vertex Covering and Feedback Vertex Set,MVCFVS),并给出了具体的仿真实验和分析。实验结果表明,与最新的算法比较,该算法得到的节点集在多种模型下都具有优异的传播效果,例如在独立级联模型和加权级联模型中超过当前最好的算法,并且还具有更快的收敛速度。 Influence maximization is an optimization issue of finding a subset of nodes under a given diffusion model, which can maximize the spread of influence. This optimization issue has been proved to be NP-hard. Leveraging the fact that vertices in minimum vertex covering and feedback vertex set are of great importance for the connectivity of a graph, a heuristic algorithm for influence maximization based on Minimum Vertex Covering and Feedback Vertex Set（MVCFVS）. Extensive experiments on various diffusion models against state of the art algorithms are carried out. Specifically, the proposed algorithm performs excellent on Independent Cascade Model（ICM） and Weighted Cascade Model（WCM）, which exhibits its great advantages in terms of influence range and convergent speed.
作者 许宇光 潘惊治 谢惠扬
机构地区 北京大学信息科学技术学院 北京林业大学理学院
出处 《电子与信息学报》 EI CSCD 北大核心 2016年第4期795-802,共8页 Journal of Electronics & Information Technology
基金 国家自然科学基金(61370193)~~
关键词 社交网络 影响最大化 传播模型 最小点覆盖 反馈点集 Social network Influence maximization Diffusion models Minimum vertex covering Feedback vertex set
分类号 TP393 [自动化与计算机技术—计算机应用技术]
作者简介 许宇光：男，1984年生，博士生，研究方向为计算机软件与理论． 潘惊治：女，1992年生，硕士生，研究方向为社交网络． 通信作者：谢惠扬：女，1963年生，教授，研究方向为应用数学．谢惠扬xhyang@bjfu．edu．cn
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参考文献28

  • 1WATTS D J and STROGATZ S H.Collective dynamics of 'small-world' networks[J].Nature,1998,393(6684):440-442.
  • 2BARABASI A L and ALBERT R.Emergence of scaling in random networks[J].Science,1999,286(5439):509-512.
  • 3SAITO K,NAKANA R,and KIMURA M.Prediction of information diffusion probabilities for independent cascade model[J].Lecture Notes in Computer Science,2008,5179:67-75.
  • 4TANG J,SUN J,and YANG Z.Social influence analysis in large-scale networks[C].Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining,New York,USA,2009:807-816.
  • 5GOYAL A,BONCHI F,and LASKHMANAN L.Learning influence probabilities in social networks[C].Proceedings of the Third ACM International Conference on Web Search & Data Mining,New York,USA,2010:241-250.
  • 6WANG C,TANG J,SUN J,et al.Dynamic social influence analysis through time-dependent factor graphs[C].Proceedings of the 2011 International Conference on Advances in Social Networks Analysis and Mining,Washington,DC,USA,2011:239-246.
  • 7DOMIGOS P and RICHARDSON M.Mining the network value of customers[C].Proceedings of the Seventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining,San Francisco,USA,2001:57-66.
  • 8RICHARDSON M and DOMINGOS P.Mining knowledge- sharing sites for viral marketing[C].Proceedings of the Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining,Alberta,Canada,2002:61-70.
  • 9KEMPE D,KLEINBERG J,and TARDOS E.Influential nodes in a diffusion model for social networks[J].International Colloquium on Automata,Languages and Programming,2005,32:1127-1138.
  • 10KEMPE D,KLEINBERG J,and TARDOS E.Maximizing the spread of influence in a social network[C].Proceedings of the 9th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining,Washington,USA,2003:137-146.

二级参考文献26

  • 1Domingos P, Richardson M. Mining the network value of customers//Proceedings of the 7th ACM SIGKDD Interna- tional Conference on Knowledge Discovery and Data Mining. San Francisco, USA, 2001: 57-66.
  • 2Richardson M, Domingos P. Mining knowledge-sharing sites for viral marketing//Proeeedings of the 8th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Edmonton, Canada, 2002:61-70.
  • 3Kempe D, Kleinberg J, Tardos L. Maximizing the spread of influence through a social network//Proceedings of the 9th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Washington DC, USA, 2003: 137-146.
  • 4Leskovec J, Krause A, Guestrin C, et al. Cost-effective out- break detection in networks//Proeeedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. San Jose, USA, 2007:420-429.
  • 5Chen Wei, Wang Ya-Jun, Yang Si-Yu. Efficient influence maximization in social networks//Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Paris, France, 2009: 199-207.
  • 6Estavez Pablo A, Vera P, Saito K. Selecting the most influential nodes in social networks//Proceedings of the 2007 International Joint Conference on Neural Networks. Orlando, USA, 2007:2397-2402.
  • 7Chen Wei, Wang Chi, Wang Ya-Jun. Scalable influence maximization for prevalent viral marketing in large-scale social networks//Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Washington DC, USA, 2010:1029-1038.
  • 8Chen Wei, Yuan Yi-Fei, Zhang Li. Scalable influence maxi- mization in social networks under the linear threshold model //Proceedings of the 10th IEEE International Conference on Data Mining. Sydney, Australia, 2010:88-97.
  • 9Chen Wei, Collins A, Cummings R, et al. Influence maximi- zation in social networks when negative opinions may emerge and propagate//Proceedings of the llth SIAM International Conference on Data Mining. Mesa, USA, 2011:379 -390.
  • 10Narayanam R, Narahari Y. A shapley value-based approach to discover influential nodes in social networks. IEEE Transactions on Automation Science and Engineering, 2010, 8(1): 130- 147.

共引文献55

同被引文献46

引证文献7

二级引证文献11

电子与信息学报
2016年 第4期

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