基于关键节点的影响力最大化算法被引量：2

Influence of Key-Nodes Based Maximization Algorithm

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摘要为解决LDAG(DAG Algorithm Based on Linear Threshold)算法在处理关于社会网络影响力最大化过程中,优先考虑网络影响力传播模型、忽视社会网络的拓扑结构问题,利用社交网络社区的结构,有针对性地选择影响力传播的关键节点,对LDAG算法进行了改进。利用关键节点简化了有向无环图的构造过程,保证了其高精度与运行效率高的特点,同时也优化了算法的时间复杂度和空间复杂度。通过两个有效的实验数据集对算法进行验证,结果表明改进的算法可以大幅度降低算法的运行时间,且对算法的精度影响很小。 LDAG( DAG algorithm based on linear threshold) algorithm is a heuristic algorithm for maximizing the influence of social networks. It has the characteristics of high accuracy and high efficiency. When solving the problem of maximizing the influence of social networks,the network influence propagation model is given priority,and then the topological structure of social networks is ignored. In this paper,the LDAG algorithm is improved by using the structure of social network community to select the key nodes of influence propagation.The key nodes are used to simplify the construction process of directed acyclic graph,optimize the time complexity and space complexity of the algorithm,and validate the rationality of the algorithm by using two effective experimental data sets.

作者王越群于健邹跃鹏李永丽董立岩 WANG Yuequn;YU Jian;ZOU Yunpeng;LI Yongli;DONG Liyan(College of Computer Science and Technology,Jilin University,Changchun 130012,China;Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education,Jilin University,Changchun 130012,China;School of Information Science and Technology,Northeast Normal University,Changchun 130117,China)

机构地区吉林大学计算机科学与技术学院东北师范大学信息科学与技术学院吉林大学符号计算与知识工程教育部重点实验室

出处《吉林大学学报（信息科学版）》 CAS 2019年第2期162-167,共6页 Journal of Jilin University（Information Science Edition）

基金国家自然科学基金资助项目(61272209)

关键词社交网络关键节点 LDAG算法 social network key nodes LDAG algorithm

分类号 TP301.6 [自动化与计算机技术—计算机系统结构]

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参考文献4

1赵之滢,于海,朱志良,汪小帆.基于网络社团结构的节点传播影响力分析[J].计算机学报,2014,37(4):753-766. 被引量：41
2李国良,楚娅萍,冯建华,徐尧强.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. 被引量：33
3陈浩,王轶彤.基于阈值的社交网络影响力最大化算法[J].计算机研究与发展,2012,49(10):2181-2188. 被引量：22
4田家堂,王轶彤,冯小军.一种新型的社会网络影响最大化算法[J].计算机学报,2011,34(10):1956-1965. 被引量：44

二级参考文献85

1Kempe D, Kleinberg J, Tardos E. Maximizing the spread of influence in a social network//Proceedings of the 9th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Washington, USA, 2003: 137-146.
2Chen Wei, Wang Ya Jun, Yang Si Yu. Efficient influence maximization in social networks//Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Dis covery and Data Mining. Paris, France, 2009:199 208.
3Young H P. The diffusion of innovations in social net works//Blume L, Durlauf S. The Economy as a Complex System III. USA: Oxford University Press, 2003:1-19.
4Watts D J. A simple model of global cascades on random net works. National Academy of Sciences, 2002, 99(9): 5766- 5571.
5Goldenberg J, Libai B, Muller E. Talk of the network: A complex systems look at the underlying process of word-of- mouth. Marketing Letters, 2001, 12(3): 211-223.
6Estevez Pablo A, Vera Pablo, Saito Kazumi. Selecting the most influential nodes in social networks//Proceedings of the International Joint Conference on Neural Networks. Orlando, Florida, USA, 2007:2397-2402.
7Suri N Rama, Narahari Y. Determining the top k nodes in social networks using the shapely value (Short Paper)//Proceedings of the 7th International Joint Con{erence on Autono mous Agents and Multiagent Systems. Estoril, Portugal, 2008:1509-1512.
8Wang YiTong, Feng XiaoJun. A potential-based node selection strategy for influence maximization in a social network//Proceedings of the 5th International Conference on Advanced Data Mining and Applications. Bering, China, 2009: 350-361.
9Leskovec J, Huttenlocher D, Kleinberg J. Signed networks in social media//Proceedings of the 28th International Conference on Human Factors in Computing Systems. Atlanta, USA, 2010: 1361 -1370.
10Sun Shi-Wei, Ling Lun-Jiang, Zhang Nan, Li Guo-Jie, Chen Run-Sheng. Topological structure analysis of the proteinprotein interaction network in budding yeast. Nucleic Acids Research, 2003, 31(9): 2443-2450.